Overview

Brought to you by YData

Dataset statistics

Number of variables49
Number of observations375141
Missing cells2757840
Missing cells (%)15.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory700.8 MiB
Average record size in memory1.9 KiB

Variable types

Text20
Numeric19
Categorical6
DateTime1
URL2
Boolean1

Alerts

type has constant value "audio_features" Constant
rn has constant value "1" Constant
time_signature is highly imbalanced (65.7%) Imbalance
artist_1 has 288062 (76.8%) missing values Missing
artist_2 has 336841 (89.8%) missing values Missing
artist_3 has 361318 (96.3%) missing values Missing
artist_4 has 369054 (98.4%) missing values Missing
genre_0 has 165278 (44.1%) missing values Missing
genre_1 has 255655 (68.1%) missing values Missing
genre_2 has 297922 (79.4%) missing values Missing
genre_3 has 326531 (87.0%) missing values Missing
genre_4 has 343824 (91.7%) missing values Missing
duration_sec is highly skewed (γ1 = 24.88087409) Skewed
track_id has unique values Unique
album_popularity has 74198 (19.8%) zeros Zeros
artist_popularity has 46397 (12.4%) zeros Zeros
instrumentalness has 108294 (28.9%) zeros Zeros
key has 43670 (11.6%) zeros Zeros
track_popularity has 147239 (39.2%) zeros Zeros

Reproduction

Analysis started2025-04-07 19:55:52.678046
Analysis finished2025-04-07 19:58:26.200056
Duration2 minutes and 33.52 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Distinct67991
Distinct (%)18.1%
Missing0
Missing (%)0.0%
Memory size28.3 MiB
2025-04-07T16:58:26.442564image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters8253102
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35273 ?
Unique (%)9.4%

Sample

1st row4zH8bbEjLe0Gdbv2FVLjnK
2nd row4zH8bbEjLe0Gdbv2FVLjnK
3rd row4zH8bbEjLe0Gdbv2FVLjnK
4th row4zH8bbEjLe0Gdbv2FVLjnK
5th row4zH8bbEjLe0Gdbv2FVLjnK
ValueCountFrequency (%)
6qgauugdqobffpxbvdh5zp 50
 
< 0.1%
2ygbt5bylzy79kl5uresar 50
 
< 0.1%
0eyyegll3nnpsboyvs1pse 50
 
< 0.1%
0skwkkd8iysax45qgynmxw 50
 
< 0.1%
26e5up17wramsy8in8fxfn 50
 
< 0.1%
6clxhs4xwevf4lvbivtmvx 50
 
< 0.1%
3eiddelchciocgpvhbvdgr 50
 
< 0.1%
7zhvuvtxdqteqxwd0qpyc1 50
 
< 0.1%
4y7loxdpysjdjumc7de3tj 50
 
< 0.1%
6nk5kuoe2htosmsmi10mpl 50
 
< 0.1%
Other values (67981) 374641
99.9%
2025-04-07T16:58:26.797621image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 179568
 
2.2%
4 176785
 
2.1%
0 176243
 
2.1%
1 175266
 
2.1%
5 174672
 
2.1%
3 174474
 
2.1%
2 174246
 
2.1%
7 164886
 
2.0%
e 130397
 
1.6%
K 129545
 
1.6%
Other values (52) 6597020
79.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8253102
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6 179568
 
2.2%
4 176785
 
2.1%
0 176243
 
2.1%
1 175266
 
2.1%
5 174672
 
2.1%
3 174474
 
2.1%
2 174246
 
2.1%
7 164886
 
2.0%
e 130397
 
1.6%
K 129545
 
1.6%
Other values (52) 6597020
79.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8253102
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6 179568
 
2.2%
4 176785
 
2.1%
0 176243
 
2.1%
1 175266
 
2.1%
5 174672
 
2.1%
3 174474
 
2.1%
2 174246
 
2.1%
7 164886
 
2.0%
e 130397
 
1.6%
K 129545
 
1.6%
Other values (52) 6597020
79.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8253102
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6 179568
 
2.2%
4 176785
 
2.1%
0 176243
 
2.1%
1 175266
 
2.1%
5 174672
 
2.1%
3 174474
 
2.1%
2 174246
 
2.1%
7 164886
 
2.0%
e 130397
 
1.6%
K 129545
 
1.6%
Other values (52) 6597020
79.9%
Distinct61467
Distinct (%)16.4%
Missing1
Missing (%)< 0.1%
Memory size32.2 MiB
2025-04-07T16:58:27.081063image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length303
Median length191
Mean length27.877225
Min length1

Characters and Unicode

Total characters10457862
Distinct characters1809
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30260 ?
Unique (%)8.1%

Sample

1st rowSon Of Tabu
2nd rowSon Of Tabu
3rd rowSon Of Tabu
4th rowSon Of Tabu
5th rowSon Of Tabu
ValueCountFrequency (%)
the 75826
 
4.3%
62591
 
3.5%
of 37047
 
2.1%
in 19119
 
1.1%
karaoke 18995
 
1.1%
to 18920
 
1.1%
2 17264
 
1.0%
vol 16870
 
0.9%
a 16574
 
0.9%
and 14697
 
0.8%
Other values (33981) 1479194
83.2%
2025-04-07T16:58:27.541516image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1401913
 
13.4%
e 882683
 
8.4%
a 599648
 
5.7%
o 592869
 
5.7%
i 578901
 
5.5%
n 537521
 
5.1%
r 495184
 
4.7%
t 418396
 
4.0%
s 387952
 
3.7%
l 369148
 
3.5%
Other values (1799) 4193647
40.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10457862
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1401913
 
13.4%
e 882683
 
8.4%
a 599648
 
5.7%
o 592869
 
5.7%
i 578901
 
5.5%
n 537521
 
5.1%
r 495184
 
4.7%
t 418396
 
4.0%
s 387952
 
3.7%
l 369148
 
3.5%
Other values (1799) 4193647
40.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10457862
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1401913
 
13.4%
e 882683
 
8.4%
a 599648
 
5.7%
o 592869
 
5.7%
i 578901
 
5.5%
n 537521
 
5.1%
r 495184
 
4.7%
t 418396
 
4.0%
s 387952
 
3.7%
l 369148
 
3.5%
Other values (1799) 4193647
40.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10457862
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1401913
 
13.4%
e 882683
 
8.4%
a 599648
 
5.7%
o 592869
 
5.7%
i 578901
 
5.5%
n 537521
 
5.1%
r 495184
 
4.7%
t 418396
 
4.0%
s 387952
 
3.7%
l 369148
 
3.5%
Other values (1799) 4193647
40.1%

album_popularity
Real number (ℝ)

Zeros 

Distinct99
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.13202
Minimum0
Maximum100
Zeros74198
Zeros (%)19.8%
Negative0
Negative (%)0.0%
Memory size2.9 MiB
2025-04-07T16:58:27.698162image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median8
Q328
95-th percentile62
Maximum100
Range100
Interquartile range (IQR)27

Descriptive statistics

Standard deviation20.371022
Coefficient of variation (CV)1.1890613
Kurtosis0.81286298
Mean17.13202
Median Absolute Deviation (MAD)8
Skewness1.2869481
Sum6426923
Variance414.97853
MonotonicityNot monotonic
2025-04-07T16:58:27.851048image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 74198
19.8%
1 34489
 
9.2%
2 20297
 
5.4%
3 15216
 
4.1%
4 11668
 
3.1%
5 10391
 
2.8%
6 8569
 
2.3%
7 8440
 
2.2%
8 7467
 
2.0%
11 6542
 
1.7%
Other values (89) 177864
47.4%
ValueCountFrequency (%)
0 74198
19.8%
1 34489
9.2%
2 20297
 
5.4%
3 15216
 
4.1%
4 11668
 
3.1%
5 10391
 
2.8%
6 8569
 
2.3%
7 8440
 
2.2%
8 7467
 
2.0%
9 6189
 
1.6%
ValueCountFrequency (%)
100 22
 
< 0.1%
98 21
 
< 0.1%
97 17
 
< 0.1%
95 46
< 0.1%
94 26
 
< 0.1%
93 10
 
< 0.1%
92 68
< 0.1%
91 78
< 0.1%
90 11
 
< 0.1%
89 109
< 0.1%

album_type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size22.3 MiB
album
297589 
single
77552 

Length

Max length6
Median length5
Mean length5.2067276
Min length5

Characters and Unicode

Total characters1953257
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowalbum
2nd rowalbum
3rd rowalbum
4th rowalbum
5th rowalbum

Common Values

ValueCountFrequency (%)
album 297589
79.3%
single 77552
 
20.7%

Length

2025-04-07T16:58:27.989871image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-07T16:58:28.119141image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
album 297589
79.3%
single 77552
 
20.7%

Most occurring characters

ValueCountFrequency (%)
l 375141
19.2%
a 297589
15.2%
b 297589
15.2%
u 297589
15.2%
m 297589
15.2%
s 77552
 
4.0%
i 77552
 
4.0%
n 77552
 
4.0%
g 77552
 
4.0%
e 77552
 
4.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1953257
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 375141
19.2%
a 297589
15.2%
b 297589
15.2%
u 297589
15.2%
m 297589
15.2%
s 77552
 
4.0%
i 77552
 
4.0%
n 77552
 
4.0%
g 77552
 
4.0%
e 77552
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1953257
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 375141
19.2%
a 297589
15.2%
b 297589
15.2%
u 297589
15.2%
m 297589
15.2%
s 77552
 
4.0%
i 77552
 
4.0%
n 77552
 
4.0%
g 77552
 
4.0%
e 77552
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1953257
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 375141
19.2%
a 297589
15.2%
b 297589
15.2%
u 297589
15.2%
m 297589
15.2%
s 77552
 
4.0%
i 77552
 
4.0%
n 77552
 
4.0%
g 77552
 
4.0%
e 77552
 
4.0%
Distinct66002
Distinct (%)17.6%
Missing0
Missing (%)0.0%
Memory size30.7 MiB
2025-04-07T16:58:28.396678image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length661
Median length300
Mean length24.827761
Min length4

Characters and Unicode

Total characters9313911
Distinct characters1393
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39611 ?
Unique (%)10.6%

Sample

1st row['AB']
2nd row['AB', '22nd Jim']
3rd row['AB', 'Veeze']
4th row['AB']
5th row['AB']
ValueCountFrequency (%)
the 26962
 
2.3%
karaoke 15631
 
1.3%
9264
 
0.8%
music 8900
 
0.8%
miguel 7604
 
0.7%
of 7411
 
0.6%
sound 6349
 
0.5%
orchestra 5539
 
0.5%
band 5182
 
0.4%
lil 4965
 
0.4%
Other values (52677) 1067454
91.6%
2025-04-07T16:58:28.882538image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 1053942
 
11.3%
790120
 
8.5%
e 611816
 
6.6%
a 586210
 
6.3%
i 434836
 
4.7%
n 428456
 
4.6%
o 424873
 
4.6%
r 400797
 
4.3%
] 375161
 
4.0%
[ 375161
 
4.0%
Other values (1383) 3832539
41.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9313911
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 1053942
 
11.3%
790120
 
8.5%
e 611816
 
6.6%
a 586210
 
6.3%
i 434836
 
4.7%
n 428456
 
4.6%
o 424873
 
4.6%
r 400797
 
4.3%
] 375161
 
4.0%
[ 375161
 
4.0%
Other values (1383) 3832539
41.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9313911
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 1053942
 
11.3%
790120
 
8.5%
e 611816
 
6.6%
a 586210
 
6.3%
i 434836
 
4.7%
n 428456
 
4.6%
o 424873
 
4.6%
r 400797
 
4.3%
] 375161
 
4.0%
[ 375161
 
4.0%
Other values (1383) 3832539
41.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9313911
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 1053942
 
11.3%
790120
 
8.5%
e 611816
 
6.6%
a 586210
 
6.3%
i 434836
 
4.7%
n 428456
 
4.6%
o 424873
 
4.6%
r 400797
 
4.3%
] 375161
 
4.0%
[ 375161
 
4.0%
Other values (1383) 3832539
41.1%
Distinct35112
Distinct (%)9.4%
Missing2
Missing (%)< 0.1%
Memory size25.7 MiB
2025-04-07T16:58:29.388554image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length112
Median length76
Mean length13.314974
Min length1

Characters and Unicode

Total characters4994966
Distinct characters1144
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15458 ?
Unique (%)4.1%

Sample

1st rowAB
2nd rowAB
3rd rowAB
4th rowAB
5th rowAB
ValueCountFrequency (%)
the 22023
 
2.7%
karaoke 15629
 
1.9%
7748
 
0.9%
miguel 7340
 
0.9%
music 6795
 
0.8%
band 4975
 
0.6%
johnny 4211
 
0.5%
piano 4109
 
0.5%
ameritz 3836
 
0.5%
anthony 3700
 
0.5%
Other values (30943) 736929
90.2%
2025-04-07T16:58:29.806883image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
442199
 
8.9%
e 416740
 
8.3%
a 411730
 
8.2%
i 298783
 
6.0%
o 297494
 
6.0%
n 293986
 
5.9%
r 271875
 
5.4%
l 207189
 
4.1%
s 183914
 
3.7%
t 183224
 
3.7%
Other values (1134) 1987832
39.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4994966
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
442199
 
8.9%
e 416740
 
8.3%
a 411730
 
8.2%
i 298783
 
6.0%
o 297494
 
6.0%
n 293986
 
5.9%
r 271875
 
5.4%
l 207189
 
4.1%
s 183914
 
3.7%
t 183224
 
3.7%
Other values (1134) 1987832
39.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4994966
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
442199
 
8.9%
e 416740
 
8.3%
a 411730
 
8.2%
i 298783
 
6.0%
o 297494
 
6.0%
n 293986
 
5.9%
r 271875
 
5.4%
l 207189
 
4.1%
s 183914
 
3.7%
t 183224
 
3.7%
Other values (1134) 1987832
39.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4994966
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
442199
 
8.9%
e 416740
 
8.3%
a 411730
 
8.2%
i 298783
 
6.0%
o 297494
 
6.0%
n 293986
 
5.9%
r 271875
 
5.4%
l 207189
 
4.1%
s 183914
 
3.7%
t 183224
 
3.7%
Other values (1134) 1987832
39.8%

artist_1
Text

Missing 

Distinct24641
Distinct (%)28.3%
Missing288062
Missing (%)76.8%
Memory size14.8 MiB
2025-04-07T16:58:30.036966image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length85
Median length64
Mean length13.133396
Min length1

Characters and Unicode

Total characters1143643
Distinct characters659
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16092 ?
Unique (%)18.5%

Sample

1st row22nd Jim
2nd rowVeeze
3rd rowZogno
4th rowJay Africano
5th rowRicochet
ValueCountFrequency (%)
the 2888
 
1.6%
of 2135
 
1.2%
sound 1870
 
1.0%
center 1545
 
0.8%
choir 1496
 
0.8%
healing 1481
 
0.8%
st 1277
 
0.7%
music 1168
 
0.6%
1057
 
0.6%
lil 1044
 
0.6%
Other values (24137) 169460
91.4%
2025-04-07T16:58:30.447716image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 99103
 
8.7%
98344
 
8.6%
a 90414
 
7.9%
i 72064
 
6.3%
n 69785
 
6.1%
o 66661
 
5.8%
r 63635
 
5.6%
l 46867
 
4.1%
s 41480
 
3.6%
t 40928
 
3.6%
Other values (649) 454362
39.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1143643
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 99103
 
8.7%
98344
 
8.6%
a 90414
 
7.9%
i 72064
 
6.3%
n 69785
 
6.1%
o 66661
 
5.8%
r 63635
 
5.6%
l 46867
 
4.1%
s 41480
 
3.6%
t 40928
 
3.6%
Other values (649) 454362
39.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1143643
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 99103
 
8.7%
98344
 
8.6%
a 90414
 
7.9%
i 72064
 
6.3%
n 69785
 
6.1%
o 66661
 
5.8%
r 63635
 
5.6%
l 46867
 
4.1%
s 41480
 
3.6%
t 40928
 
3.6%
Other values (649) 454362
39.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1143643
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 99103
 
8.7%
98344
 
8.6%
a 90414
 
7.9%
i 72064
 
6.3%
n 69785
 
6.1%
o 66661
 
5.8%
r 63635
 
5.6%
l 46867
 
4.1%
s 41480
 
3.6%
t 40928
 
3.6%
Other values (649) 454362
39.7%

artist_2
Text

Missing 

Distinct9567
Distinct (%)25.0%
Missing336841
Missing (%)89.8%
Memory size13.0 MiB
2025-04-07T16:58:30.721394image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length76
Median length49
Mean length15.174021
Min length1

Characters and Unicode

Total characters581165
Distinct characters308
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6048 ?
Unique (%)15.8%

Sample

1st rowHelko
2nd rowRicochet
3rd rowRom3
4th rowSpliff Roland
5th rowRickeyLynnPianist
ValueCountFrequency (%)
sound 1663
 
1.8%
orchestra 1508
 
1.6%
of 1332
 
1.5%
center 1312
 
1.4%
healing 1310
 
1.4%
de 1061
 
1.2%
the 1024
 
1.1%
sounds 915
 
1.0%
music 840
 
0.9%
nature 828
 
0.9%
Other values (10946) 79846
87.1%
2025-04-07T16:58:31.178914image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
53339
 
9.2%
e 53165
 
9.1%
a 46210
 
8.0%
n 36015
 
6.2%
i 35986
 
6.2%
o 33486
 
5.8%
r 32871
 
5.7%
t 23731
 
4.1%
l 23527
 
4.0%
s 22394
 
3.9%
Other values (298) 220441
37.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 581165
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
53339
 
9.2%
e 53165
 
9.1%
a 46210
 
8.0%
n 36015
 
6.2%
i 35986
 
6.2%
o 33486
 
5.8%
r 32871
 
5.7%
t 23731
 
4.1%
l 23527
 
4.0%
s 22394
 
3.9%
Other values (298) 220441
37.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 581165
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
53339
 
9.2%
e 53165
 
9.1%
a 46210
 
8.0%
n 36015
 
6.2%
i 35986
 
6.2%
o 33486
 
5.8%
r 32871
 
5.7%
t 23731
 
4.1%
l 23527
 
4.0%
s 22394
 
3.9%
Other values (298) 220441
37.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 581165
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
53339
 
9.2%
e 53165
 
9.1%
a 46210
 
8.0%
n 36015
 
6.2%
i 35986
 
6.2%
o 33486
 
5.8%
r 32871
 
5.7%
t 23731
 
4.1%
l 23527
 
4.0%
s 22394
 
3.9%
Other values (298) 220441
37.9%

artist_3
Text

Missing 

Distinct3828
Distinct (%)27.7%
Missing361318
Missing (%)96.3%
Memory size12.0 MiB
2025-04-07T16:58:31.431373image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length79
Median length46
Mean length14.923606
Min length1

Characters and Unicode

Total characters206289
Distinct characters155
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2285 ?
Unique (%)16.5%

Sample

1st rowAkim Akena
2nd rowFastCash Jizzle
3rd rowMc Menor Da Ms
4th rowMrs Dv
5th rowRIKI
ValueCountFrequency (%)
orchestra 846
 
2.7%
of 464
 
1.5%
the 457
 
1.5%
symphony 332
 
1.1%
st 318
 
1.0%
choir 306
 
1.0%
john 256
 
0.8%
sir 252
 
0.8%
george 238
 
0.8%
martin 222
 
0.7%
Other values (5121) 27751
88.3%
2025-04-07T16:58:31.868926image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 18243
 
8.8%
17619
 
8.5%
a 16348
 
7.9%
r 14283
 
6.9%
n 12871
 
6.2%
i 12814
 
6.2%
o 12036
 
5.8%
l 8767
 
4.2%
s 8292
 
4.0%
t 7988
 
3.9%
Other values (145) 77028
37.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 206289
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 18243
 
8.8%
17619
 
8.5%
a 16348
 
7.9%
r 14283
 
6.9%
n 12871
 
6.2%
i 12814
 
6.2%
o 12036
 
5.8%
l 8767
 
4.2%
s 8292
 
4.0%
t 7988
 
3.9%
Other values (145) 77028
37.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 206289
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 18243
 
8.8%
17619
 
8.5%
a 16348
 
7.9%
r 14283
 
6.9%
n 12871
 
6.2%
i 12814
 
6.2%
o 12036
 
5.8%
l 8767
 
4.2%
s 8292
 
4.0%
t 7988
 
3.9%
Other values (145) 77028
37.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 206289
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 18243
 
8.8%
17619
 
8.5%
a 16348
 
7.9%
r 14283
 
6.9%
n 12871
 
6.2%
i 12814
 
6.2%
o 12036
 
5.8%
l 8767
 
4.2%
s 8292
 
4.0%
t 7988
 
3.9%
Other values (145) 77028
37.3%

artist_4
Text

Missing 

Distinct1756
Distinct (%)28.8%
Missing369054
Missing (%)98.4%
Memory size11.7 MiB
2025-04-07T16:58:32.109711image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length79
Median length44
Mean length14.904386
Min length1

Characters and Unicode

Total characters90723
Distinct characters121
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1036 ?
Unique (%)17.0%

Sample

1st rowJus'
2nd rowFastCash CMoney
3rd rowPaTra Freshy
4th rowLil Wil
5th rowH
ValueCountFrequency (%)
orchestra 211
 
1.5%
of 210
 
1.5%
james 164
 
1.2%
the 152
 
1.1%
st 136
 
1.0%
stephen 122
 
0.9%
john 119
 
0.9%
choir 109
 
0.8%
mark 98
 
0.7%
george 98
 
0.7%
Other values (2603) 12397
89.7%
2025-04-07T16:58:32.527242image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 8495
 
9.4%
7729
 
8.5%
a 7455
 
8.2%
r 6115
 
6.7%
n 5597
 
6.2%
o 5487
 
6.0%
i 5006
 
5.5%
t 3928
 
4.3%
l 3820
 
4.2%
s 3436
 
3.8%
Other values (111) 33655
37.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 90723
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 8495
 
9.4%
7729
 
8.5%
a 7455
 
8.2%
r 6115
 
6.7%
n 5597
 
6.2%
o 5487
 
6.0%
i 5006
 
5.5%
t 3928
 
4.3%
l 3820
 
4.2%
s 3436
 
3.8%
Other values (111) 33655
37.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 90723
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 8495
 
9.4%
7729
 
8.5%
a 7455
 
8.2%
r 6115
 
6.7%
n 5597
 
6.2%
o 5487
 
6.0%
i 5006
 
5.5%
t 3928
 
4.3%
l 3820
 
4.2%
s 3436
 
3.8%
Other values (111) 33655
37.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 90723
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 8495
 
9.4%
7729
 
8.5%
a 7455
 
8.2%
r 6115
 
6.7%
n 5597
 
6.2%
o 5487
 
6.0%
i 5006
 
5.5%
t 3928
 
4.3%
l 3820
 
4.2%
s 3436
 
3.8%
Other values (111) 33655
37.1%
Distinct31699
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Memory size28.3 MiB
2025-04-07T16:58:32.734189image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters8253102
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12989 ?
Unique (%)3.5%

Sample

1st row08jcfs5JspUBqM3rbFNUzm
2nd row08jcfs5JspUBqM3rbFNUzm
3rd row08jcfs5JspUBqM3rbFNUzm
4th row08jcfs5JspUBqM3rbFNUzm
5th row08jcfs5JspUBqM3rbFNUzm
ValueCountFrequency (%)
5aiqb5nvvvmfsvsdexz408 2292
 
0.6%
6kacvpfconqzgfef5ryl0x 1897
 
0.5%
5ulovkzunjcjorzhhtwwij 1763
 
0.5%
3ctgjaslnn3ym8shm5jujn 1455
 
0.4%
2o5jdhthvphrjdv3ceq99z 1442
 
0.4%
6lcthtphtxxcydjz1izjuv 1350
 
0.4%
796osrub0e9hq55utfl9u8 1342
 
0.4%
0fqntkf71zmqozmb1ulput 1285
 
0.3%
06hl4z0cvfaxyc27gxpf02 938
 
0.3%
46mfshagvgedatk3aixg4l 937
 
0.2%
Other values (31689) 360440
96.1%
2025-04-07T16:58:33.058601image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 188870
 
2.3%
6 182853
 
2.2%
4 179969
 
2.2%
5 174242
 
2.1%
3 171796
 
2.1%
2 167570
 
2.0%
1 167131
 
2.0%
7 163904
 
2.0%
J 135703
 
1.6%
h 135142
 
1.6%
Other values (52) 6585922
79.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8253102
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 188870
 
2.3%
6 182853
 
2.2%
4 179969
 
2.2%
5 174242
 
2.1%
3 171796
 
2.1%
2 167570
 
2.0%
1 167131
 
2.0%
7 163904
 
2.0%
J 135703
 
1.6%
h 135142
 
1.6%
Other values (52) 6585922
79.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8253102
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 188870
 
2.3%
6 182853
 
2.2%
4 179969
 
2.2%
5 174242
 
2.1%
3 171796
 
2.1%
2 167570
 
2.0%
1 167131
 
2.0%
7 163904
 
2.0%
J 135703
 
1.6%
h 135142
 
1.6%
Other values (52) 6585922
79.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8253102
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 188870
 
2.3%
6 182853
 
2.2%
4 179969
 
2.2%
5 174242
 
2.1%
3 171796
 
2.1%
2 167570
 
2.0%
1 167131
 
2.0%
7 163904
 
2.0%
J 135703
 
1.6%
h 135142
 
1.6%
Other values (52) 6585922
79.8%

duration_sec
Real number (ℝ)

Skewed 

Distinct138290
Distinct (%)36.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean215.42777
Minimum0
Maximum17919
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.9 MiB
2025-04-07T16:58:33.210360image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile82.152
Q1159.7
median204.545
Q3251.48
95-th percentile372.173
Maximum17919
Range17919
Interquartile range (IQR)91.78

Descriptive statistics

Standard deviation125.72742
Coefficient of variation (CV)0.58361752
Kurtosis2392.8917
Mean215.42777
Median Absolute Deviation (MAD)45.868
Skewness24.880874
Sum80815789
Variance15807.385
MonotonicityNot monotonic
2025-04-07T16:58:33.362132image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120 1629
 
0.4%
192 336
 
0.1%
180 283
 
0.1%
240 277
 
0.1%
60 212
 
0.1%
150 192
 
0.1%
160 175
 
< 0.1%
216 170
 
< 0.1%
144 164
 
< 0.1%
96 162
 
< 0.1%
Other values (138280) 371541
99.0%
ValueCountFrequency (%)
0 2
< 0.1%
0.184 1
< 0.1%
0.737 1
< 0.1%
0.798 1
< 0.1%
0.85 1
< 0.1%
1.858 1
< 0.1%
2.127 1
< 0.1%
2.131 1
< 0.1%
2.205 1
< 0.1%
2.508 1
< 0.1%
ValueCountFrequency (%)
17919 1
< 0.1%
17040 1
< 0.1%
12813.333 1
< 0.1%
7295.5 1
< 0.1%
5280.026 1
< 0.1%
5014.686 1
< 0.1%
4945.293 1
< 0.1%
4777.826 1
< 0.1%
4737.986 1
< 0.1%
4669.72 1
< 0.1%

label
Text

Distinct29638
Distinct (%)7.9%
Missing52
Missing (%)< 0.1%
Memory size26.4 MiB
2025-04-07T16:58:33.620095image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length149
Median length96
Mean length15.945344
Min length1

Characters and Unicode

Total characters5980923
Distinct characters804
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12185 ?
Unique (%)3.2%

Sample

1st rowAbout Benjamin Records
2nd rowAbout Benjamin Records
3rd rowAbout Benjamin Records
4th rowAbout Benjamin Records
5th rowAbout Benjamin Records
ValueCountFrequency (%)
records 86381
 
9.5%
music 56637
 
6.3%
entertainment 12311
 
1.4%
11895
 
1.3%
ltd 11338
 
1.3%
dk 11022
 
1.2%
recordings 8807
 
1.0%
group 8388
 
0.9%
llc 8324
 
0.9%
the 7501
 
0.8%
Other values (26389) 682446
75.4%
2025-04-07T16:58:34.061627image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
529961
 
8.9%
e 452823
 
7.6%
o 359120
 
6.0%
r 344163
 
5.8%
i 343961
 
5.8%
a 324176
 
5.4%
s 317224
 
5.3%
n 288832
 
4.8%
c 263666
 
4.4%
t 230870
 
3.9%
Other values (794) 2526127
42.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5980923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
529961
 
8.9%
e 452823
 
7.6%
o 359120
 
6.0%
r 344163
 
5.8%
i 343961
 
5.8%
a 324176
 
5.4%
s 317224
 
5.3%
n 288832
 
4.8%
c 263666
 
4.4%
t 230870
 
3.9%
Other values (794) 2526127
42.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5980923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
529961
 
8.9%
e 452823
 
7.6%
o 359120
 
6.0%
r 344163
 
5.8%
i 343961
 
5.8%
a 324176
 
5.4%
s 317224
 
5.3%
n 288832
 
4.8%
c 263666
 
4.4%
t 230870
 
3.9%
Other values (794) 2526127
42.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5980923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
529961
 
8.9%
e 452823
 
7.6%
o 359120
 
6.0%
r 344163
 
5.8%
i 343961
 
5.8%
a 324176
 
5.4%
s 317224
 
5.3%
n 288832
 
4.8%
c 263666
 
4.4%
t 230870
 
3.9%
Other values (794) 2526127
42.2%
Distinct7503
Distinct (%)2.0%
Missing22
Missing (%)< 0.1%
Memory size2.9 MiB
Minimum1886-01-01 00:00:00+00:00
Maximum2023-12-22 00:00:00+00:00
Invalid dates0
Invalid dates (%)0.0%
2025-04-07T16:58:34.212227image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:58:34.354002image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

total_tracks
Real number (ℝ)

Distinct151
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.258711
Minimum1
Maximum984
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 MiB
2025-04-07T16:58:34.499011image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q18
median13
Q320
95-th percentile50
Maximum984
Range983
Interquartile range (IQR)12

Descriptive statistics

Standard deviation26.674688
Coefficient of variation (CV)1.4609295
Kurtosis429.39888
Mean18.258711
Median Absolute Deviation (MAD)6
Skewness14.571685
Sum6849591
Variance711.53899
MonotonicityNot monotonic
2025-04-07T16:58:34.648653image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 34736
 
9.3%
10 30071
 
8.0%
12 27286
 
7.3%
11 19864
 
5.3%
14 18289
 
4.9%
13 17333
 
4.6%
15 16858
 
4.5%
16 14168
 
3.8%
8 11356
 
3.0%
20 11320
 
3.0%
Other values (141) 173860
46.3%
ValueCountFrequency (%)
1 34736
9.3%
2 9838
 
2.6%
3 7354
 
2.0%
4 6829
 
1.8%
5 7908
 
2.1%
6 9254
 
2.5%
7 8139
 
2.2%
8 11356
 
3.0%
9 11265
 
3.0%
10 30071
8.0%
ValueCountFrequency (%)
984 50
< 0.1%
928 50
< 0.1%
389 50
< 0.1%
336 50
< 0.1%
283 50
< 0.1%
274 50
< 0.1%
247 50
< 0.1%
244 50
< 0.1%
243 50
< 0.1%
239 50
< 0.1%

track_id
Text

Unique 

Distinct375141
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size28.3 MiB
2025-04-07T16:58:35.021665image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters8253102
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique375141 ?
Unique (%)100.0%

Sample

1st row5JucnTkqh8uCZGggL1xxvv
2nd row4VWrKNG3ssyZeboTgQ7Kj1
3rd row6JsVrTLJPSOSpgJSnZyfSK
4th row7pZpw1xJWIavCUGqXPPriP
5th row0ylqwc3gcJdJotKd4SfMiu
ValueCountFrequency (%)
5jucntkqh8uczgggl1xxvv 1
 
< 0.1%
6jsvrtljpsospgjsnzyfsk 1
 
< 0.1%
0ylqwc3gcjdjotkd4sfmiu 1
 
< 0.1%
33prxoje4bkggcsvop6it6 1
 
< 0.1%
3fxaezpdnw3x0uxpwkucpl 1
 
< 0.1%
50tz5mxmfwevbz5btmcmmi 1
 
< 0.1%
4izfhrlg0qk0xspodsanbh 1
 
< 0.1%
21ep4am1asi5fn43gpnes1 1
 
< 0.1%
5a4pbcktf3a0fxrwyrgi5p 1
 
< 0.1%
33vntbqnz3p6ygc876has1 1
 
< 0.1%
Other values (375131) 375131
> 99.9%
2025-04-07T16:58:35.550512image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 175737
 
2.1%
5 175517
 
2.1%
0 175437
 
2.1%
1 175310
 
2.1%
3 175299
 
2.1%
4 175090
 
2.1%
6 174946
 
2.1%
7 165541
 
2.0%
b 128034
 
1.6%
a 127875
 
1.5%
Other values (52) 6604316
80.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8253102
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 175737
 
2.1%
5 175517
 
2.1%
0 175437
 
2.1%
1 175310
 
2.1%
3 175299
 
2.1%
4 175090
 
2.1%
6 174946
 
2.1%
7 165541
 
2.0%
b 128034
 
1.6%
a 127875
 
1.5%
Other values (52) 6604316
80.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8253102
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 175737
 
2.1%
5 175517
 
2.1%
0 175437
 
2.1%
1 175310
 
2.1%
3 175299
 
2.1%
4 175090
 
2.1%
6 174946
 
2.1%
7 165541
 
2.0%
b 128034
 
1.6%
a 127875
 
1.5%
Other values (52) 6604316
80.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8253102
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 175737
 
2.1%
5 175517
 
2.1%
0 175437
 
2.1%
1 175310
 
2.1%
3 175299
 
2.1%
4 175090
 
2.1%
6 174946
 
2.1%
7 165541
 
2.0%
b 128034
 
1.6%
a 127875
 
1.5%
Other values (52) 6604316
80.0%
Distinct275523
Distinct (%)73.4%
Missing5
Missing (%)< 0.1%
Memory size30.2 MiB
2025-04-07T16:58:35.906515image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length343
Median length242
Mean length23.327345
Min length1

Characters and Unicode

Total characters8750927
Distinct characters3562
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique238357 ?
Unique (%)63.5%

Sample

1st rowIntro
2nd rowPlay It Cool
3rd row3 Peat
4th rowAffiliated
5th rowSpringfield
ValueCountFrequency (%)
78043
 
4.9%
the 50526
 
3.2%
of 23686
 
1.5%
in 22436
 
1.4%
version 22339
 
1.4%
you 16086
 
1.0%
i 14706
 
0.9%
a 14115
 
0.9%
karaoke 14030
 
0.9%
me 12478
 
0.8%
Other values (102673) 1322296
83.1%
2025-04-07T16:58:36.444617image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1215578
 
13.9%
e 766086
 
8.8%
a 512341
 
5.9%
o 492285
 
5.6%
i 446132
 
5.1%
n 445458
 
5.1%
r 415633
 
4.7%
t 352915
 
4.0%
l 283882
 
3.2%
s 277766
 
3.2%
Other values (3552) 3542851
40.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8750927
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1215578
 
13.9%
e 766086
 
8.8%
a 512341
 
5.9%
o 492285
 
5.6%
i 446132
 
5.1%
n 445458
 
5.1%
r 415633
 
4.7%
t 352915
 
4.0%
l 283882
 
3.2%
s 277766
 
3.2%
Other values (3552) 3542851
40.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8750927
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1215578
 
13.9%
e 766086
 
8.8%
a 512341
 
5.9%
o 492285
 
5.6%
i 446132
 
5.1%
n 445458
 
5.1%
r 415633
 
4.7%
t 352915
 
4.0%
l 283882
 
3.2%
s 277766
 
3.2%
Other values (3552) 3542851
40.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8750927
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1215578
 
13.9%
e 766086
 
8.8%
a 512341
 
5.9%
o 492285
 
5.6%
i 446132
 
5.1%
n 445458
 
5.1%
r 415633
 
4.7%
t 352915
 
4.0%
l 283882
 
3.2%
s 277766
 
3.2%
Other values (3552) 3542851
40.5%

track_number
Real number (ℝ)

Distinct50
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.0393665
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 MiB
2025-04-07T16:58:36.822122image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median6
Q311
95-th percentile24
Maximum50
Range49
Interquartile range (IQR)9

Descriptive statistics

Standard deviation8.0892649
Coefficient of variation (CV)1.0062068
Kurtosis5.8870638
Mean8.0393665
Median Absolute Deviation (MAD)4
Skewness2.1583865
Sum3015896
Variance65.436207
MonotonicityNot monotonic
2025-04-07T16:58:37.005949image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 67976
18.1%
2 33415
 
8.9%
3 28501
 
7.6%
4 25982
 
6.9%
5 24210
 
6.5%
6 22584
 
6.0%
7 20984
 
5.6%
8 19807
 
5.3%
9 18280
 
4.9%
10 16943
 
4.5%
Other values (40) 96459
25.7%
ValueCountFrequency (%)
1 67976
18.1%
2 33415
8.9%
3 28501
7.6%
4 25982
 
6.9%
5 24210
 
6.5%
6 22584
 
6.0%
7 20984
 
5.6%
8 19807
 
5.3%
9 18280
 
4.9%
10 16943
 
4.5%
ValueCountFrequency (%)
50 379
0.1%
49 392
0.1%
48 397
0.1%
47 414
0.1%
46 423
0.1%
45 442
0.1%
44 444
0.1%
43 457
0.1%
42 476
0.1%
41 481
0.1%
Distinct5993
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size28.1 MiB
2025-04-07T16:58:37.219319image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length218
Median length199
Mean length21.416385
Min length2

Characters and Unicode

Total characters8034164
Distinct characters44
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1121 ?
Unique (%)0.3%

Sample

1st row[]
2nd row[]
3rd row[]
4th row[]
5th row[]
ValueCountFrequency (%)
165278
 
15.1%
pop 63314
 
5.8%
rock 51019
 
4.6%
rap 41723
 
3.8%
hip 36123
 
3.3%
hop 35057
 
3.2%
country 21462
 
2.0%
classical 18741
 
1.7%
karaoke 15505
 
1.4%
metal 12822
 
1.2%
Other values (2094) 636202
58.0%
2025-04-07T16:58:37.603427image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 1036082
 
12.9%
722105
 
9.0%
a 547658
 
6.8%
o 503034
 
6.3%
r 444808
 
5.5%
e 394316
 
4.9%
[ 375141
 
4.7%
] 375141
 
4.7%
p 362957
 
4.5%
n 330971
 
4.1%
Other values (34) 2941951
36.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8034164
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 1036082
 
12.9%
722105
 
9.0%
a 547658
 
6.8%
o 503034
 
6.3%
r 444808
 
5.5%
e 394316
 
4.9%
[ 375141
 
4.7%
] 375141
 
4.7%
p 362957
 
4.5%
n 330971
 
4.1%
Other values (34) 2941951
36.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8034164
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 1036082
 
12.9%
722105
 
9.0%
a 547658
 
6.8%
o 503034
 
6.3%
r 444808
 
5.5%
e 394316
 
4.9%
[ 375141
 
4.7%
] 375141
 
4.7%
p 362957
 
4.5%
n 330971
 
4.1%
Other values (34) 2941951
36.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8034164
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 1036082
 
12.9%
722105
 
9.0%
a 547658
 
6.8%
o 503034
 
6.3%
r 444808
 
5.5%
e 394316
 
4.9%
[ 375141
 
4.7%
] 375141
 
4.7%
p 362957
 
4.5%
n 330971
 
4.1%
Other values (34) 2941951
36.6%

artist_popularity
Real number (ℝ)

Zeros 

Distinct94
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.346552
Minimum0
Maximum100
Zeros46397
Zeros (%)12.4%
Negative0
Negative (%)0.0%
Memory size2.9 MiB
2025-04-07T16:58:37.756629image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median24
Q348
95-th percentile79
Maximum100
Range100
Interquartile range (IQR)43

Descriptive statistics

Standard deviation26.250589
Coefficient of variation (CV)0.89450332
Kurtosis-0.69037169
Mean29.346552
Median Absolute Deviation (MAD)20
Skewness0.66395303
Sum11009095
Variance689.0934
MonotonicityIncreasing
2025-04-07T16:58:37.911184image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 46397
 
12.4%
1 16746
 
4.5%
2 10475
 
2.8%
4 8699
 
2.3%
3 8447
 
2.3%
32 8231
 
2.2%
22 7251
 
1.9%
5 7063
 
1.9%
6 6457
 
1.7%
29 5815
 
1.6%
Other values (84) 249560
66.5%
ValueCountFrequency (%)
0 46397
12.4%
1 16746
 
4.5%
2 10475
 
2.8%
3 8447
 
2.3%
4 8699
 
2.3%
5 7063
 
1.9%
6 6457
 
1.7%
7 5209
 
1.4%
8 5098
 
1.4%
9 4147
 
1.1%
ValueCountFrequency (%)
100 938
0.3%
95 785
0.2%
93 407
 
0.1%
90 403
 
0.1%
89 1009
0.3%
88 1448
0.4%
87 398
 
0.1%
86 1373
0.4%
85 1495
0.4%
84 1160
0.3%

followers
Real number (ℝ)

Distinct12001
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2282645.6
Minimum0
Maximum1.1603564 × 108
Zeros3009
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size2.9 MiB
2025-04-07T16:58:38.055150image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12
Q1250
median3511
Q398161
95-th percentile10790145
Maximum1.1603564 × 108
Range1.1603564 × 108
Interquartile range (IQR)97911

Descriptive statistics

Standard deviation9635134.6
Coefficient of variation (CV)4.2210384
Kurtosis55.288337
Mean2282645.6
Median Absolute Deviation (MAD)3497
Skewness7.0039867
Sum8.5631394 × 1011
Variance9.2835819 × 1013
MonotonicityNot monotonic
2025-04-07T16:58:38.209464image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3009
 
0.8%
4216717 2003
 
0.5%
1 2001
 
0.5%
6120030 1897
 
0.5%
2 1713
 
0.5%
4 1539
 
0.4%
8 1502
 
0.4%
6 1489
 
0.4%
3 1467
 
0.4%
7229863 1437
 
0.4%
Other values (11991) 357084
95.2%
ValueCountFrequency (%)
0 3009
0.8%
1 2001
0.5%
2 1713
0.5%
3 1467
0.4%
4 1539
0.4%
5 1241
0.3%
6 1489
0.4%
7 1436
0.4%
8 1502
0.4%
9 1032
 
0.3%
ValueCountFrequency (%)
116035640 2
 
< 0.1%
116016052 6
 
< 0.1%
115998928 6
 
< 0.1%
95859165 938
0.3%
95741850 18
 
< 0.1%
95710972 230
 
0.1%
89996504 70
 
< 0.1%
83298497 648
0.2%
79891173 742
0.2%
77931484 137
 
< 0.1%

name
Text

Distinct30948
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Memory size25.8 MiB
2025-04-07T16:58:38.440915image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length193
Median length75
Mean length13.402409
Min length1

Characters and Unicode

Total characters5027793
Distinct characters1034
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12662 ?
Unique (%)3.4%

Sample

1st rowAB
2nd rowAB
3rd rowAB
4th rowAB
5th rowAB
ValueCountFrequency (%)
the 22045
 
2.7%
karaoke 15678
 
1.9%
8187
 
1.0%
miguel 7378
 
0.9%
music 6903
 
0.8%
band 4970
 
0.6%
johnny 4370
 
0.5%
piano 4101
 
0.5%
ameritz 3869
 
0.5%
anthony 3726
 
0.5%
Other values (27637) 741262
90.1%
2025-04-07T16:58:38.859206image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
447391
 
8.9%
e 418112
 
8.3%
a 412756
 
8.2%
o 302190
 
6.0%
i 300753
 
6.0%
n 293051
 
5.8%
r 273851
 
5.4%
l 205932
 
4.1%
s 186095
 
3.7%
t 184898
 
3.7%
Other values (1024) 2002764
39.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5027793
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
447391
 
8.9%
e 418112
 
8.3%
a 412756
 
8.2%
o 302190
 
6.0%
i 300753
 
6.0%
n 293051
 
5.8%
r 273851
 
5.4%
l 205932
 
4.1%
s 186095
 
3.7%
t 184898
 
3.7%
Other values (1024) 2002764
39.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5027793
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
447391
 
8.9%
e 418112
 
8.3%
a 412756
 
8.2%
o 302190
 
6.0%
i 300753
 
6.0%
n 293051
 
5.8%
r 273851
 
5.4%
l 205932
 
4.1%
s 186095
 
3.7%
t 184898
 
3.7%
Other values (1024) 2002764
39.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5027793
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
447391
 
8.9%
e 418112
 
8.3%
a 412756
 
8.2%
o 302190
 
6.0%
i 300753
 
6.0%
n 293051
 
5.8%
r 273851
 
5.4%
l 205932
 
4.1%
s 186095
 
3.7%
t 184898
 
3.7%
Other values (1024) 2002764
39.8%

genre_0
Text

Missing 

Distinct3008
Distinct (%)1.4%
Missing165278
Missing (%)44.1%
Memory size18.7 MiB
2025-04-07T16:58:39.059077image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length33
Median length28
Mean length11.350548
Min length2

Characters and Unicode

Total characters2382060
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique366 ?
Unique (%)0.2%

Sample

1st rowindie hip hop
2nd rowaustralian hardcore
3rd rowaustralian hardcore
4th rowaustralian hardcore
5th rowaustralian hardcore
ValueCountFrequency (%)
pop 23770
 
6.2%
hip 15956
 
4.2%
hop 15396
 
4.0%
karaoke 15344
 
4.0%
rock 11063
 
2.9%
country 8317
 
2.2%
classical 8057
 
2.1%
rap 7514
 
2.0%
classic 6894
 
1.8%
alternative 6181
 
1.6%
Other values (1763) 262544
68.9%
2025-04-07T16:58:39.411792image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 254287
 
10.7%
o 208826
 
8.8%
171173
 
7.2%
r 169173
 
7.1%
e 163373
 
6.9%
i 154335
 
6.5%
n 136741
 
5.7%
p 136729
 
5.7%
c 131722
 
5.5%
s 120175
 
5.0%
Other values (30) 735526
30.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2382060
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 254287
 
10.7%
o 208826
 
8.8%
171173
 
7.2%
r 169173
 
7.1%
e 163373
 
6.9%
i 154335
 
6.5%
n 136741
 
5.7%
p 136729
 
5.7%
c 131722
 
5.5%
s 120175
 
5.0%
Other values (30) 735526
30.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2382060
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 254287
 
10.7%
o 208826
 
8.8%
171173
 
7.2%
r 169173
 
7.1%
e 163373
 
6.9%
i 154335
 
6.5%
n 136741
 
5.7%
p 136729
 
5.7%
c 131722
 
5.5%
s 120175
 
5.0%
Other values (30) 735526
30.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2382060
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 254287
 
10.7%
o 208826
 
8.8%
171173
 
7.2%
r 169173
 
7.1%
e 163373
 
6.9%
i 154335
 
6.5%
n 136741
 
5.7%
p 136729
 
5.7%
c 131722
 
5.5%
s 120175
 
5.0%
Other values (30) 735526
30.9%

genre_1
Text

Missing 

Distinct1740
Distinct (%)1.5%
Missing255655
Missing (%)68.1%
Memory size15.6 MiB
2025-04-07T16:58:39.652827image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length33
Median length27
Mean length11.29647
Min length2

Characters and Unicode

Total characters1349770
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique220 ?
Unique (%)0.2%

Sample

1st rowprogressive sludge
2nd rowprogressive sludge
3rd rowprogressive sludge
4th rowprogressive sludge
5th rowprogressive sludge
ValueCountFrequency (%)
pop 16642
 
7.6%
rock 9812
 
4.5%
rap 9049
 
4.1%
classical 8143
 
3.7%
hip 7338
 
3.4%
country 7282
 
3.3%
hop 7267
 
3.3%
classic 2935
 
1.3%
jazz 2913
 
1.3%
metal 2798
 
1.3%
Other values (1120) 144128
66.0%
2025-04-07T16:58:40.042835image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 128329
 
9.5%
o 117171
 
8.7%
r 100186
 
7.4%
98821
 
7.3%
e 94632
 
7.0%
p 91031
 
6.7%
n 83046
 
6.2%
i 82154
 
6.1%
c 79561
 
5.9%
s 74084
 
5.5%
Other values (27) 400755
29.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1349770
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 128329
 
9.5%
o 117171
 
8.7%
r 100186
 
7.4%
98821
 
7.3%
e 94632
 
7.0%
p 91031
 
6.7%
n 83046
 
6.2%
i 82154
 
6.1%
c 79561
 
5.9%
s 74084
 
5.5%
Other values (27) 400755
29.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1349770
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 128329
 
9.5%
o 117171
 
8.7%
r 100186
 
7.4%
98821
 
7.3%
e 94632
 
7.0%
p 91031
 
6.7%
n 83046
 
6.2%
i 82154
 
6.1%
c 79561
 
5.9%
s 74084
 
5.5%
Other values (27) 400755
29.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1349770
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 128329
 
9.5%
o 117171
 
8.7%
r 100186
 
7.4%
98821
 
7.3%
e 94632
 
7.0%
p 91031
 
6.7%
n 83046
 
6.2%
i 82154
 
6.1%
c 79561
 
5.9%
s 74084
 
5.5%
Other values (27) 400755
29.7%

genre_2
Text

Missing 

Distinct955
Distinct (%)1.2%
Missing297922
Missing (%)79.4%
Memory size14.1 MiB
2025-04-07T16:58:40.237144image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length30
Median length25
Mean length10.496355
Min length3

Characters and Unicode

Total characters810518
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique120 ?
Unique (%)0.2%

Sample

1st rowfrench metal
2nd rowfrench metal
3rd rowfrench metal
4th rowfrench metal
5th rowfrench metal
ValueCountFrequency (%)
rock 10373
 
7.4%
pop 10239
 
7.4%
rap 8627
 
6.2%
hip 5736
 
4.1%
hop 5511
 
4.0%
early 4193
 
3.0%
music 3757
 
2.7%
country 3753
 
2.7%
edm 2445
 
1.8%
contemporary 2193
 
1.6%
Other values (706) 82419
59.2%
2025-04-07T16:58:40.568249image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 73658
 
9.1%
o 70820
 
8.7%
r 70529
 
8.7%
62027
 
7.7%
e 55766
 
6.9%
p 54284
 
6.7%
n 48946
 
6.0%
c 47852
 
5.9%
i 44743
 
5.5%
s 37175
 
4.6%
Other values (23) 244718
30.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 810518
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 73658
 
9.1%
o 70820
 
8.7%
r 70529
 
8.7%
62027
 
7.7%
e 55766
 
6.9%
p 54284
 
6.7%
n 48946
 
6.0%
c 47852
 
5.9%
i 44743
 
5.5%
s 37175
 
4.6%
Other values (23) 244718
30.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 810518
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 73658
 
9.1%
o 70820
 
8.7%
r 70529
 
8.7%
62027
 
7.7%
e 55766
 
6.9%
p 54284
 
6.7%
n 48946
 
6.0%
c 47852
 
5.9%
i 44743
 
5.5%
s 37175
 
4.6%
Other values (23) 244718
30.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 810518
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 73658
 
9.1%
o 70820
 
8.7%
r 70529
 
8.7%
62027
 
7.7%
e 55766
 
6.9%
p 54284
 
6.7%
n 48946
 
6.0%
c 47852
 
5.9%
i 44743
 
5.5%
s 37175
 
4.6%
Other values (23) 244718
30.2%

genre_3
Text

Missing 

Distinct524
Distinct (%)1.1%
Missing326531
Missing (%)87.0%
Memory size13.1 MiB
2025-04-07T16:58:40.752698image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length30
Median length24
Mean length9.8970994
Min length3

Characters and Unicode

Total characters481098
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique65 ?
Unique (%)0.1%

Sample

1st rownu electro
2nd rownu electro
3rd rownu electro
4th rownu electro
5th rownu electro
ValueCountFrequency (%)
rap 8346
 
9.9%
pop 6129
 
7.3%
rock 5841
 
6.9%
hip 3242
 
3.9%
hop 3223
 
3.8%
baroque 3048
 
3.6%
german 2641
 
3.1%
metal 1963
 
2.3%
country 1561
 
1.9%
edm 1492
 
1.8%
Other values (424) 46705
55.5%
2025-04-07T16:58:41.086510image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 46475
 
9.7%
o 44692
 
9.3%
a 42782
 
8.9%
e 36143
 
7.5%
35581
 
7.4%
p 35511
 
7.4%
n 28876
 
6.0%
i 24271
 
5.0%
c 22231
 
4.6%
s 22112
 
4.6%
Other values (23) 142424
29.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 481098
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 46475
 
9.7%
o 44692
 
9.3%
a 42782
 
8.9%
e 36143
 
7.5%
35581
 
7.4%
p 35511
 
7.4%
n 28876
 
6.0%
i 24271
 
5.0%
c 22231
 
4.6%
s 22112
 
4.6%
Other values (23) 142424
29.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 481098
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 46475
 
9.7%
o 44692
 
9.3%
a 42782
 
8.9%
e 36143
 
7.5%
35581
 
7.4%
p 35511
 
7.4%
n 28876
 
6.0%
i 24271
 
5.0%
c 22231
 
4.6%
s 22112
 
4.6%
Other values (23) 142424
29.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 481098
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 46475
 
9.7%
o 44692
 
9.3%
a 42782
 
8.9%
e 36143
 
7.5%
35581
 
7.4%
p 35511
 
7.4%
n 28876
 
6.0%
i 24271
 
5.0%
c 22231
 
4.6%
s 22112
 
4.6%
Other values (23) 142424
29.6%

genre_4
Text

Missing 

Distinct301
Distinct (%)1.0%
Missing343824
Missing (%)91.7%
Memory size12.5 MiB
2025-04-07T16:58:41.334406image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length30
Median length23
Mean length9.129674
Min length3

Characters and Unicode

Total characters285914
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)0.1%

Sample

1st rowmexican experimental
2nd rowmexican experimental
3rd rowmexican experimental
4th rowmexican experimental
5th rowmexican experimental
ValueCountFrequency (%)
rock 6690
 
12.7%
rap 4585
 
8.7%
pop 2595
 
4.9%
trap 2473
 
4.7%
hip 2007
 
3.8%
hop 1770
 
3.4%
house 1739
 
3.3%
jazz 1380
 
2.6%
southern 1234
 
2.4%
vocal 1021
 
1.9%
Other values (278) 27011
51.4%
2025-04-07T16:58:41.706562image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 29240
 
10.2%
o 28788
 
10.1%
a 23924
 
8.4%
21188
 
7.4%
p 20630
 
7.2%
e 20229
 
7.1%
n 15891
 
5.6%
c 15010
 
5.2%
t 14811
 
5.2%
s 13337
 
4.7%
Other values (21) 82866
29.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 285914
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 29240
 
10.2%
o 28788
 
10.1%
a 23924
 
8.4%
21188
 
7.4%
p 20630
 
7.2%
e 20229
 
7.1%
n 15891
 
5.6%
c 15010
 
5.2%
t 14811
 
5.2%
s 13337
 
4.7%
Other values (21) 82866
29.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 285914
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 29240
 
10.2%
o 28788
 
10.1%
a 23924
 
8.4%
21188
 
7.4%
p 20630
 
7.2%
e 20229
 
7.1%
n 15891
 
5.6%
c 15010
 
5.2%
t 14811
 
5.2%
s 13337
 
4.7%
Other values (21) 82866
29.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 285914
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 29240
 
10.2%
o 28788
 
10.1%
a 23924
 
8.4%
21188
 
7.4%
p 20630
 
7.2%
e 20229
 
7.1%
n 15891
 
5.6%
c 15010
 
5.2%
t 14811
 
5.2%
s 13337
 
4.7%
Other values (21) 82866
29.0%

acousticness
Real number (ℝ)

Distinct5242
Distinct (%)1.4%
Missing777
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean0.3856914
Minimum0
Maximum0.996
Zeros85
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.9 MiB
2025-04-07T16:58:41.862668image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.00055
Q10.0389
median0.268
Q30.739
95-th percentile0.981
Maximum0.996
Range0.996
Interquartile range (IQR)0.7001

Descriptive statistics

Standard deviation0.35907155
Coefficient of variation (CV)0.93098149
Kurtosis-1.3638665
Mean0.3856914
Median Absolute Deviation (MAD)0.26005
Skewness0.44681473
Sum144388.98
Variance0.12893238
MonotonicityNot monotonic
2025-04-07T16:58:42.018574image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.995 2007
 
0.5%
0.994 1688
 
0.4%
0.993 1644
 
0.4%
0.992 1605
 
0.4%
0.991 1356
 
0.4%
0.99 1305
 
0.3%
0.989 1201
 
0.3%
0.988 1139
 
0.3%
0.987 1098
 
0.3%
0.986 1021
 
0.3%
Other values (5232) 360300
96.0%
ValueCountFrequency (%)
0 85
< 0.1%
1.01 × 10-61
 
< 0.1%
1.03 × 10-62
 
< 0.1%
1.04 × 10-62
 
< 0.1%
1.05 × 10-61
 
< 0.1%
1.07 × 10-63
 
< 0.1%
1.09 × 10-64
 
< 0.1%
1.1 × 10-62
 
< 0.1%
1.11 × 10-61
 
< 0.1%
1.13 × 10-62
 
< 0.1%
ValueCountFrequency (%)
0.996 705
 
0.2%
0.995 2007
0.5%
0.994 1688
0.4%
0.993 1644
0.4%
0.992 1605
0.4%
0.991 1356
0.4%
0.99 1305
0.3%
0.989 1201
0.3%
0.988 1139
0.3%
0.987 1098
0.3%
Distinct374364
Distinct (%)100.0%
Missing777
Missing (%)0.2%
Memory size43.2 MiB
https://api.spotify.com/v1/audio-analysis/5JucnTkqh8uCZGggL1xxvv
 
1
https://api.spotify.com/v1/audio-analysis/78ZPhDz7YFOlZsbU0f1q0V
 
1
https://api.spotify.com/v1/audio-analysis/0gJjIkAa4OnoPzsjd0qVns
 
1
https://api.spotify.com/v1/audio-analysis/0YxRx1G4GNqCJdkop9CrFP
 
1
https://api.spotify.com/v1/audio-analysis/00nfNF7ngkLd3YJnWDKzxy
 
1
Other values (374359)
374359 
(Missing)
 
777
ValueCountFrequency (%)
https://api.spotify.com/v1/audio-analysis/5JucnTkqh8uCZGggL1xxvv 1
 
< 0.1%
https://api.spotify.com/v1/audio-analysis/78ZPhDz7YFOlZsbU0f1q0V 1
 
< 0.1%
https://api.spotify.com/v1/audio-analysis/0gJjIkAa4OnoPzsjd0qVns 1
 
< 0.1%
https://api.spotify.com/v1/audio-analysis/0YxRx1G4GNqCJdkop9CrFP 1
 
< 0.1%
https://api.spotify.com/v1/audio-analysis/00nfNF7ngkLd3YJnWDKzxy 1
 
< 0.1%
https://api.spotify.com/v1/audio-analysis/4Dc5ELq4tC01Hw9Dw3OjbZ 1
 
< 0.1%
https://api.spotify.com/v1/audio-analysis/07OwCPXg2PeJdg90OJVhx1 1
 
< 0.1%
https://api.spotify.com/v1/audio-analysis/4MLy2zIPdn5gl6SpTWQYAG 1
 
< 0.1%
https://api.spotify.com/v1/audio-analysis/5l2SG4BkEgyXdgc0zk63sO 1
 
< 0.1%
https://api.spotify.com/v1/audio-analysis/5Dhi5QwTGAOLqnqs1VvCHj 1
 
< 0.1%
Other values (374354) 374354
99.8%
(Missing) 777
 
0.2%
ValueCountFrequency (%)
https 374364
99.8%
(Missing) 777
 
0.2%
ValueCountFrequency (%)
api.spotify.com 374364
99.8%
(Missing) 777
 
0.2%
ValueCountFrequency (%)
/v1/audio-analysis/5JucnTkqh8uCZGggL1xxvv 1
 
< 0.1%
/v1/audio-analysis/3essuepQGkhmThd037oiwp 1
 
< 0.1%
/v1/audio-analysis/78ZPhDz7YFOlZsbU0f1q0V 1
 
< 0.1%
/v1/audio-analysis/0gJjIkAa4OnoPzsjd0qVns 1
 
< 0.1%
/v1/audio-analysis/0YxRx1G4GNqCJdkop9CrFP 1
 
< 0.1%
/v1/audio-analysis/00nfNF7ngkLd3YJnWDKzxy 1
 
< 0.1%
/v1/audio-analysis/4Dc5ELq4tC01Hw9Dw3OjbZ 1
 
< 0.1%
/v1/audio-analysis/07OwCPXg2PeJdg90OJVhx1 1
 
< 0.1%
/v1/audio-analysis/4MLy2zIPdn5gl6SpTWQYAG 1
 
< 0.1%
/v1/audio-analysis/5l2SG4BkEgyXdgc0zk63sO 1
 
< 0.1%
Other values (374354) 374354
99.8%
(Missing) 777
 
0.2%
ValueCountFrequency (%)
374364
99.8%
(Missing) 777
 
0.2%
ValueCountFrequency (%)
374364
99.8%
(Missing) 777
 
0.2%

danceability
Real number (ℝ)

Distinct1327
Distinct (%)0.4%
Missing777
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean0.57232931
Minimum0
Maximum0.999
Zeros546
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size2.9 MiB
2025-04-07T16:58:42.172471image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.203
Q10.452
median0.598
Q30.714
95-th percentile0.844
Maximum0.999
Range0.999
Interquartile range (IQR)0.262

Descriptive statistics

Standard deviation0.19081584
Coefficient of variation (CV)0.33340217
Kurtosis-0.30498305
Mean0.57232931
Median Absolute Deviation (MAD)0.128
Skewness-0.50775559
Sum214259.49
Variance0.036410684
MonotonicityNot monotonic
2025-04-07T16:58:42.324846image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.624 885
 
0.2%
0.712 878
 
0.2%
0.625 876
 
0.2%
0.668 872
 
0.2%
0.715 871
 
0.2%
0.676 869
 
0.2%
0.671 865
 
0.2%
0.647 864
 
0.2%
0.645 863
 
0.2%
0.693 859
 
0.2%
Other values (1317) 365662
97.5%
ValueCountFrequency (%)
0 546
0.1%
0.0167 1
 
< 0.1%
0.0236 1
 
< 0.1%
0.0492 1
 
< 0.1%
0.0532 1
 
< 0.1%
0.0554 2
 
< 0.1%
0.0558 1
 
< 0.1%
0.056 2
 
< 0.1%
0.0564 1
 
< 0.1%
0.0565 5
 
< 0.1%
ValueCountFrequency (%)
0.999 1
 
< 0.1%
0.993 1
 
< 0.1%
0.992 2
 
< 0.1%
0.989 3
 
< 0.1%
0.988 7
< 0.1%
0.987 7
< 0.1%
0.986 3
 
< 0.1%
0.985 4
 
< 0.1%
0.984 4
 
< 0.1%
0.983 13
< 0.1%

duration_ms
Real number (ℝ)

Distinct137997
Distinct (%)36.9%
Missing777
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean215521.47
Minimum3056
Maximum5280027
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 MiB
2025-04-07T16:58:42.472770image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum3056
5-th percentile83107.75
Q1160000
median204707
Q3251613
95-th percentile372292.45
Maximum5280027
Range5276971
Interquartile range (IQR)91613

Descriptive statistics

Standard deviation116646.1
Coefficient of variation (CV)0.54122727
Kurtosis262.89516
Mean215521.47
Median Absolute Deviation (MAD)45752
Skewness9.8473945
Sum8.068348 × 1010
Variance1.3606312 × 1010
MonotonicityNot monotonic
2025-04-07T16:58:42.626542image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120000 1585
 
0.4%
192000 340
 
0.1%
240000 295
 
0.1%
180000 272
 
0.1%
60000 187
 
< 0.1%
160000 185
 
< 0.1%
216000 172
 
< 0.1%
144000 168
 
< 0.1%
150000 162
 
< 0.1%
208000 161
 
< 0.1%
Other values (137987) 370837
98.9%
(Missing) 777
 
0.2%
ValueCountFrequency (%)
3056 1
< 0.1%
3515 1
< 0.1%
3901 1
< 0.1%
4013 1
< 0.1%
4027 1
< 0.1%
4067 1
< 0.1%
4109 1
< 0.1%
4120 1
< 0.1%
4153 1
< 0.1%
4160 1
< 0.1%
ValueCountFrequency (%)
5280027 1
< 0.1%
5014686 1
< 0.1%
4945293 1
< 0.1%
4777827 1
< 0.1%
4737987 1
< 0.1%
4669720 1
< 0.1%
4669240 1
< 0.1%
4666560 1
< 0.1%
4636774 1
< 0.1%
4620533 1
< 0.1%

energy
Real number (ℝ)

Distinct2918
Distinct (%)0.8%
Missing777
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean0.52480864
Minimum0
Maximum1
Zeros25
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.9 MiB
2025-04-07T16:58:42.777493image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.067315
Q10.319
median0.539
Q30.736
95-th percentile0.933
Maximum1
Range1
Interquartile range (IQR)0.417

Descriptive statistics

Standard deviation0.26416953
Coefficient of variation (CV)0.50336353
Kurtosis-0.95416753
Mean0.52480864
Median Absolute Deviation (MAD)0.208
Skewness-0.16097092
Sum196469.46
Variance0.06978554
MonotonicityNot monotonic
2025-04-07T16:58:42.936223image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.539 568
 
0.2%
0.64 566
 
0.2%
0.562 554
 
0.1%
0.662 547
 
0.1%
0.528 546
 
0.1%
0.57 544
 
0.1%
0.574 532
 
0.1%
0.568 531
 
0.1%
0.556 531
 
0.1%
0.566 528
 
0.1%
Other values (2908) 368917
98.3%
(Missing) 777
 
0.2%
ValueCountFrequency (%)
0 25
< 0.1%
1.91 × 10-51
 
< 0.1%
1.97 × 10-51
 
< 0.1%
1.98 × 10-51
 
< 0.1%
2 × 10-51
 
< 0.1%
2.01 × 10-55
 
< 0.1%
2.02 × 10-56
 
< 0.1%
2.03 × 10-535
< 0.1%
2.07 × 10-51
 
< 0.1%
2.17 × 10-51
 
< 0.1%
ValueCountFrequency (%)
1 302
0.1%
0.999 435
0.1%
0.998 368
0.1%
0.997 255
0.1%
0.996 299
0.1%
0.995 268
0.1%
0.994 208
0.1%
0.993 260
0.1%
0.992 180
< 0.1%
0.991 214
0.1%

instrumentalness
Real number (ℝ)

Zeros 

Distinct5402
Distinct (%)1.4%
Missing777
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean0.26442255
Minimum0
Maximum1
Zeros108294
Zeros (%)28.9%
Negative0
Negative (%)0.0%
Memory size2.9 MiB
2025-04-07T16:58:43.322869image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.000729
Q30.698
95-th percentile0.937
Maximum1
Range1
Interquartile range (IQR)0.698

Descriptive statistics

Standard deviation0.37923663
Coefficient of variation (CV)1.4342068
Kurtosis-1.0116597
Mean0.26442255
Median Absolute Deviation (MAD)0.000729
Skewness0.9107454
Sum98990.285
Variance0.14382042
MonotonicityNot monotonic
2025-04-07T16:58:43.492448image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 108294
28.9%
0.932 674
 
0.2%
0.91 655
 
0.2%
0.915 652
 
0.2%
0.925 646
 
0.2%
0.924 641
 
0.2%
0.922 638
 
0.2%
0.923 635
 
0.2%
0.9 631
 
0.2%
0.938 629
 
0.2%
Other values (5392) 260269
69.4%
(Missing) 777
 
0.2%
ValueCountFrequency (%)
0 108294
28.9%
1 × 10-663
 
< 0.1%
1.01 × 10-6116
 
< 0.1%
1.02 × 10-6114
 
< 0.1%
1.03 × 10-6103
 
< 0.1%
1.04 × 10-6111
 
< 0.1%
1.05 × 10-695
 
< 0.1%
1.06 × 10-6101
 
< 0.1%
1.07 × 10-6133
 
< 0.1%
1.08 × 10-6118
 
< 0.1%
ValueCountFrequency (%)
1 42
< 0.1%
0.999 56
< 0.1%
0.998 45
< 0.1%
0.997 60
< 0.1%
0.996 38
< 0.1%
0.995 57
< 0.1%
0.994 84
< 0.1%
0.993 62
< 0.1%
0.992 78
< 0.1%
0.991 56
< 0.1%

key
Real number (ℝ)

Zeros 

Distinct12
Distinct (%)< 0.1%
Missing777
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean5.2303213
Minimum0
Maximum11
Zeros43670
Zeros (%)11.6%
Negative0
Negative (%)0.0%
Memory size2.9 MiB
2025-04-07T16:58:43.628529image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.5814625
Coefficient of variation (CV)0.68474999
Kurtosis-1.3080285
Mean5.2303213
Median Absolute Deviation (MAD)3
Skewness0.01986177
Sum1958044
Variance12.826873
MonotonicityNot monotonic
2025-04-07T16:58:43.767581image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 43670
11.6%
7 42404
11.3%
1 39389
10.5%
2 37951
10.1%
9 36151
9.6%
5 31666
8.4%
11 28250
7.5%
4 27586
7.4%
10 26971
7.2%
8 24049
6.4%
Other values (2) 36277
9.7%
ValueCountFrequency (%)
0 43670
11.6%
1 39389
10.5%
2 37951
10.1%
3 12874
 
3.4%
4 27586
7.4%
5 31666
8.4%
6 23403
6.2%
7 42404
11.3%
8 24049
6.4%
9 36151
9.6%
ValueCountFrequency (%)
11 28250
7.5%
10 26971
7.2%
9 36151
9.6%
8 24049
6.4%
7 42404
11.3%
6 23403
6.2%
5 31666
8.4%
4 27586
7.4%
3 12874
 
3.4%
2 37951
10.1%

liveness
Real number (ℝ)

Distinct1769
Distinct (%)0.5%
Missing777
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean0.19669092
Minimum0
Maximum1
Zeros93
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.9 MiB
2025-04-07T16:58:43.913655image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0623
Q10.0974
median0.124
Q30.235
95-th percentile0.604
Maximum1
Range1
Interquartile range (IQR)0.1376

Descriptive statistics

Standard deviation0.17503208
Coefficient of variation (CV)0.88988388
Kurtosis5.9459028
Mean0.19669092
Median Absolute Deviation (MAD)0.0408
Skewness2.3684069
Sum73633.999
Variance0.030636228
MonotonicityNot monotonic
2025-04-07T16:58:44.067809image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.111 5634
 
1.5%
0.11 5101
 
1.4%
0.109 4783
 
1.3%
0.112 4600
 
1.2%
0.108 4574
 
1.2%
0.107 4364
 
1.2%
0.106 4167
 
1.1%
0.105 4025
 
1.1%
0.104 3861
 
1.0%
0.113 3796
 
1.0%
Other values (1759) 329459
87.8%
ValueCountFrequency (%)
0 93
< 0.1%
0.00217 1
 
< 0.1%
0.00536 1
 
< 0.1%
0.0057 1
 
< 0.1%
0.00579 1
 
< 0.1%
0.00756 1
 
< 0.1%
0.00856 1
 
< 0.1%
0.00869 1
 
< 0.1%
0.00909 1
 
< 0.1%
0.00936 1
 
< 0.1%
ValueCountFrequency (%)
1 3
 
< 0.1%
0.999 2
 
< 0.1%
0.997 2
 
< 0.1%
0.996 5
 
< 0.1%
0.995 6
 
< 0.1%
0.994 10
 
< 0.1%
0.993 13
< 0.1%
0.992 12
< 0.1%
0.991 24
< 0.1%
0.99 28
< 0.1%

loudness
Real number (ℝ)

Distinct32916
Distinct (%)8.8%
Missing777
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean-11.248916
Minimum-60
Maximum3.744
Zeros1
Zeros (%)< 0.1%
Negative374145
Negative (%)99.7%
Memory size2.9 MiB
2025-04-07T16:58:44.211354image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-60
5-th percentile-24.57085
Q1-14.06
median-9.527
Q3-6.681
95-th percentile-3.977
Maximum3.744
Range63.744
Interquartile range (IQR)7.379

Descriptive statistics

Standard deviation6.5480049
Coefficient of variation (CV)-0.58210096
Kurtosis3.0730819
Mean-11.248916
Median Absolute Deviation (MAD)3.376
Skewness-1.5118694
Sum-4211189.3
Variance42.876368
MonotonicityNot monotonic
2025-04-07T16:58:44.354429image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-9.413 66
 
< 0.1%
-7.843 64
 
< 0.1%
-8.398 59
 
< 0.1%
-7.161 59
 
< 0.1%
-7.274 58
 
< 0.1%
-8.69 58
 
< 0.1%
-7.342 57
 
< 0.1%
-7.739 56
 
< 0.1%
-7.916 55
 
< 0.1%
-10.066 55
 
< 0.1%
Other values (32906) 373777
99.6%
(Missing) 777
 
0.2%
ValueCountFrequency (%)
-60 23
< 0.1%
-58.78 1
 
< 0.1%
-58.188 1
 
< 0.1%
-57.929 1
 
< 0.1%
-57.579 1
 
< 0.1%
-57.245 1
 
< 0.1%
-57.075 1
 
< 0.1%
-56.948 1
 
< 0.1%
-56.662 1
 
< 0.1%
-56.465 1
 
< 0.1%
ValueCountFrequency (%)
3.744 1
< 0.1%
3.053 1
< 0.1%
2.868 1
< 0.1%
2.853 1
< 0.1%
2.801 1
< 0.1%
2.412 1
< 0.1%
2.305 1
< 0.1%
2.24 1
< 0.1%
2.234 1
< 0.1%
2.215 1
< 0.1%

mode
Categorical

Distinct2
Distinct (%)< 0.1%
Missing777
Missing (%)0.2%
Memory size21.5 MiB
1.0
238837 
0.0
135527 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1123092
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 238837
63.7%
0.0 135527
36.1%
(Missing) 777
 
0.2%

Length

2025-04-07T16:58:44.494189image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-07T16:58:44.610100image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
1.0 238837
63.8%
0.0 135527
36.2%

Most occurring characters

ValueCountFrequency (%)
0 509891
45.4%
. 374364
33.3%
1 238837
21.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1123092
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 509891
45.4%
. 374364
33.3%
1 238837
21.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1123092
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 509891
45.4%
. 374364
33.3%
1 238837
21.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1123092
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 509891
45.4%
. 374364
33.3%
1 238837
21.3%

speechiness
Real number (ℝ)

Distinct1653
Distinct (%)0.4%
Missing777
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean0.11658458
Minimum0
Maximum0.97
Zeros545
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size2.9 MiB
2025-04-07T16:58:44.748664image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0282
Q10.0364
median0.0501
Q30.109
95-th percentile0.417
Maximum0.97
Range0.97
Interquartile range (IQR)0.0726

Descriptive statistics

Standard deviation0.16288237
Coefficient of variation (CV)1.3971176
Kurtosis11.440988
Mean0.11658458
Median Absolute Deviation (MAD)0.0182
Skewness3.1867294
Sum43645.071
Variance0.026530666
MonotonicityNot monotonic
2025-04-07T16:58:44.901702image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0315 1019
 
0.3%
0.0319 1019
 
0.3%
0.0345 988
 
0.3%
0.0339 986
 
0.3%
0.0343 981
 
0.3%
0.0318 980
 
0.3%
0.0357 979
 
0.3%
0.0332 978
 
0.3%
0.0329 978
 
0.3%
0.0317 977
 
0.3%
Other values (1643) 364479
97.2%
ValueCountFrequency (%)
0 545
0.1%
0.0217 1
 
< 0.1%
0.0218 1
 
< 0.1%
0.0221 2
 
< 0.1%
0.0222 2
 
< 0.1%
0.0223 8
 
< 0.1%
0.0224 4
 
< 0.1%
0.0225 4
 
< 0.1%
0.0226 8
 
< 0.1%
0.0227 9
 
< 0.1%
ValueCountFrequency (%)
0.97 2
 
< 0.1%
0.969 1
 
< 0.1%
0.968 17
 
< 0.1%
0.967 37
 
< 0.1%
0.966 45
< 0.1%
0.965 52
< 0.1%
0.964 50
< 0.1%
0.963 91
< 0.1%
0.962 101
< 0.1%
0.961 95
< 0.1%

tempo
Real number (ℝ)

Distinct101940
Distinct (%)27.2%
Missing777
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean118.65128
Minimum0
Maximum249.428
Zeros545
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size2.9 MiB
2025-04-07T16:58:45.047084image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile75
Q194.989
median119.422
Q3138.005
95-th percentile173.98985
Maximum249.428
Range249.428
Interquartile range (IQR)43.016

Descriptive statistics

Standard deviation30.516832
Coefficient of variation (CV)0.25719765
Kurtosis-0.072506305
Mean118.65128
Median Absolute Deviation (MAD)21.357
Skewness0.27912602
Sum44418769
Variance931.27701
MonotonicityNot monotonic
2025-04-07T16:58:45.195704image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 545
 
0.1%
120.001 139
 
< 0.1%
120.008 133
 
< 0.1%
119.999 125
 
< 0.1%
120.003 123
 
< 0.1%
120.006 118
 
< 0.1%
120.005 117
 
< 0.1%
119.996 117
 
< 0.1%
119.995 115
 
< 0.1%
130.001 114
 
< 0.1%
Other values (101930) 372718
99.4%
(Missing) 777
 
0.2%
ValueCountFrequency (%)
0 545
0.1%
30.317 1
 
< 0.1%
31.429 1
 
< 0.1%
31.522 1
 
< 0.1%
31.778 1
 
< 0.1%
31.801 1
 
< 0.1%
31.852 1
 
< 0.1%
31.917 1
 
< 0.1%
32.015 1
 
< 0.1%
32.126 1
 
< 0.1%
ValueCountFrequency (%)
249.428 1
< 0.1%
248.615 1
< 0.1%
247.989 1
< 0.1%
247.981 1
< 0.1%
247.575 1
< 0.1%
245.786 1
< 0.1%
243.837 1
< 0.1%
241.549 1
< 0.1%
241.527 1
< 0.1%
241.302 1
< 0.1%

time_signature
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing777
Missing (%)0.2%
Memory size21.5 MiB
4.0
317462 
3.0
40062 
5.0
 
11004
1.0
 
5288
0.0
 
548

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1123092
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row4.0
3rd row4.0
4th row4.0
5th row4.0

Common Values

ValueCountFrequency (%)
4.0 317462
84.6%
3.0 40062
 
10.7%
5.0 11004
 
2.9%
1.0 5288
 
1.4%
0.0 548
 
0.1%
(Missing) 777
 
0.2%

Length

2025-04-07T16:58:45.330007image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-07T16:58:45.442205image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
4.0 317462
84.8%
3.0 40062
 
10.7%
5.0 11004
 
2.9%
1.0 5288
 
1.4%
0.0 548
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 374912
33.4%
. 374364
33.3%
4 317462
28.3%
3 40062
 
3.6%
5 11004
 
1.0%
1 5288
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1123092
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 374912
33.4%
. 374364
33.3%
4 317462
28.3%
3 40062
 
3.6%
5 11004
 
1.0%
1 5288
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1123092
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 374912
33.4%
. 374364
33.3%
4 317462
28.3%
3 40062
 
3.6%
5 11004
 
1.0%
1 5288
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1123092
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 374912
33.4%
. 374364
33.3%
4 317462
28.3%
3 40062
 
3.6%
5 11004
 
1.0%
1 5288
 
0.5%
Distinct374364
Distinct (%)100.0%
Missing777
Missing (%)0.2%
Memory size40.4 MiB
https://api.spotify.com/v1/tracks/5JucnTkqh8uCZGggL1xxvv
 
1
https://api.spotify.com/v1/tracks/78ZPhDz7YFOlZsbU0f1q0V
 
1
https://api.spotify.com/v1/tracks/0gJjIkAa4OnoPzsjd0qVns
 
1
https://api.spotify.com/v1/tracks/0YxRx1G4GNqCJdkop9CrFP
 
1
https://api.spotify.com/v1/tracks/00nfNF7ngkLd3YJnWDKzxy
 
1
Other values (374359)
374359 
(Missing)
 
777
ValueCountFrequency (%)
https://api.spotify.com/v1/tracks/5JucnTkqh8uCZGggL1xxvv 1
 
< 0.1%
https://api.spotify.com/v1/tracks/78ZPhDz7YFOlZsbU0f1q0V 1
 
< 0.1%
https://api.spotify.com/v1/tracks/0gJjIkAa4OnoPzsjd0qVns 1
 
< 0.1%
https://api.spotify.com/v1/tracks/0YxRx1G4GNqCJdkop9CrFP 1
 
< 0.1%
https://api.spotify.com/v1/tracks/00nfNF7ngkLd3YJnWDKzxy 1
 
< 0.1%
https://api.spotify.com/v1/tracks/4Dc5ELq4tC01Hw9Dw3OjbZ 1
 
< 0.1%
https://api.spotify.com/v1/tracks/07OwCPXg2PeJdg90OJVhx1 1
 
< 0.1%
https://api.spotify.com/v1/tracks/4MLy2zIPdn5gl6SpTWQYAG 1
 
< 0.1%
https://api.spotify.com/v1/tracks/5l2SG4BkEgyXdgc0zk63sO 1
 
< 0.1%
https://api.spotify.com/v1/tracks/5Dhi5QwTGAOLqnqs1VvCHj 1
 
< 0.1%
Other values (374354) 374354
99.8%
(Missing) 777
 
0.2%
ValueCountFrequency (%)
https 374364
99.8%
(Missing) 777
 
0.2%
ValueCountFrequency (%)
api.spotify.com 374364
99.8%
(Missing) 777
 
0.2%
ValueCountFrequency (%)
/v1/tracks/5JucnTkqh8uCZGggL1xxvv 1
 
< 0.1%
/v1/tracks/3essuepQGkhmThd037oiwp 1
 
< 0.1%
/v1/tracks/78ZPhDz7YFOlZsbU0f1q0V 1
 
< 0.1%
/v1/tracks/0gJjIkAa4OnoPzsjd0qVns 1
 
< 0.1%
/v1/tracks/0YxRx1G4GNqCJdkop9CrFP 1
 
< 0.1%
/v1/tracks/00nfNF7ngkLd3YJnWDKzxy 1
 
< 0.1%
/v1/tracks/4Dc5ELq4tC01Hw9Dw3OjbZ 1
 
< 0.1%
/v1/tracks/07OwCPXg2PeJdg90OJVhx1 1
 
< 0.1%
/v1/tracks/4MLy2zIPdn5gl6SpTWQYAG 1
 
< 0.1%
/v1/tracks/5l2SG4BkEgyXdgc0zk63sO 1
 
< 0.1%
Other values (374354) 374354
99.8%
(Missing) 777
 
0.2%
ValueCountFrequency (%)
374364
99.8%
(Missing) 777
 
0.2%
ValueCountFrequency (%)
374364
99.8%
(Missing) 777
 
0.2%

type
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing777
Missing (%)0.2%
Memory size25.4 MiB
audio_features
374364 

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters5241096
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowaudio_features
2nd rowaudio_features
3rd rowaudio_features
4th rowaudio_features
5th rowaudio_features

Common Values

ValueCountFrequency (%)
audio_features 374364
99.8%
(Missing) 777
 
0.2%

Length

2025-04-07T16:58:45.571906image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-07T16:58:45.674973image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
audio_features 374364
100.0%

Most occurring characters

ValueCountFrequency (%)
a 748728
14.3%
u 748728
14.3%
e 748728
14.3%
d 374364
7.1%
i 374364
7.1%
o 374364
7.1%
_ 374364
7.1%
f 374364
7.1%
t 374364
7.1%
r 374364
7.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5241096
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 748728
14.3%
u 748728
14.3%
e 748728
14.3%
d 374364
7.1%
i 374364
7.1%
o 374364
7.1%
_ 374364
7.1%
f 374364
7.1%
t 374364
7.1%
r 374364
7.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5241096
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 748728
14.3%
u 748728
14.3%
e 748728
14.3%
d 374364
7.1%
i 374364
7.1%
o 374364
7.1%
_ 374364
7.1%
f 374364
7.1%
t 374364
7.1%
r 374364
7.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5241096
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 748728
14.3%
u 748728
14.3%
e 748728
14.3%
d 374364
7.1%
i 374364
7.1%
o 374364
7.1%
_ 374364
7.1%
f 374364
7.1%
t 374364
7.1%
r 374364
7.1%

uri
Text

Distinct374364
Distinct (%)100.0%
Missing777
Missing (%)0.2%
Memory size33.2 MiB
2025-04-07T16:58:46.041694image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters13477104
Distinct characters63
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique374364 ?
Unique (%)100.0%

Sample

1st rowspotify:track:5JucnTkqh8uCZGggL1xxvv
2nd rowspotify:track:4VWrKNG3ssyZeboTgQ7Kj1
3rd rowspotify:track:6JsVrTLJPSOSpgJSnZyfSK
4th rowspotify:track:7pZpw1xJWIavCUGqXPPriP
5th rowspotify:track:0ylqwc3gcJdJotKd4SfMiu
ValueCountFrequency (%)
spotify:track:5jucntkqh8uczgggl1xxvv 1
 
< 0.1%
spotify:track:6jsvrtljpsospgjsnzyfsk 1
 
< 0.1%
spotify:track:0ylqwc3gcjdjotkd4sfmiu 1
 
< 0.1%
spotify:track:33prxoje4bkggcsvop6it6 1
 
< 0.1%
spotify:track:3fxaezpdnw3x0uxpwkucpl 1
 
< 0.1%
spotify:track:50tz5mxmfwevbz5btmcmmi 1
 
< 0.1%
spotify:track:4izfhrlg0qk0xspodsanbh 1
 
< 0.1%
spotify:track:21ep4am1asi5fn43gpnes1 1
 
< 0.1%
spotify:track:5a4pbcktf3a0fxrwyrgi5p 1
 
< 0.1%
spotify:track:33vntbqnz3p6ygc876has1 1
 
< 0.1%
Other values (374354) 374354
> 99.9%
2025-04-07T16:58:46.554759image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 875424
 
6.5%
: 748728
 
5.6%
a 501969
 
3.7%
o 501595
 
3.7%
r 501442
 
3.7%
i 501433
 
3.7%
f 501432
 
3.7%
c 501367
 
3.7%
p 501351
 
3.7%
k 501321
 
3.7%
Other values (53) 7841042
58.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13477104
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 875424
 
6.5%
: 748728
 
5.6%
a 501969
 
3.7%
o 501595
 
3.7%
r 501442
 
3.7%
i 501433
 
3.7%
f 501432
 
3.7%
c 501367
 
3.7%
p 501351
 
3.7%
k 501321
 
3.7%
Other values (53) 7841042
58.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13477104
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 875424
 
6.5%
: 748728
 
5.6%
a 501969
 
3.7%
o 501595
 
3.7%
r 501442
 
3.7%
i 501433
 
3.7%
f 501432
 
3.7%
c 501367
 
3.7%
p 501351
 
3.7%
k 501321
 
3.7%
Other values (53) 7841042
58.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13477104
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 875424
 
6.5%
: 748728
 
5.6%
a 501969
 
3.7%
o 501595
 
3.7%
r 501442
 
3.7%
i 501433
 
3.7%
f 501432
 
3.7%
c 501367
 
3.7%
p 501351
 
3.7%
k 501321
 
3.7%
Other values (53) 7841042
58.2%

valence
Real number (ℝ)

Distinct2043
Distinct (%)0.5%
Missing777
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean0.4539612
Minimum0
Maximum1
Zeros845
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size2.9 MiB
2025-04-07T16:58:46.713641image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0399
Q10.231
median0.44
Q30.664
95-th percentile0.909
Maximum1
Range1
Interquartile range (IQR)0.433

Descriptive statistics

Standard deviation0.26634245
Coefficient of variation (CV)0.58670752
Kurtosis-1.0192056
Mean0.4539612
Median Absolute Deviation (MAD)0.216
Skewness0.15662897
Sum169946.73
Variance0.070938301
MonotonicityNot monotonic
2025-04-07T16:58:46.870987image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 × 10-51278
 
0.3%
0.961 943
 
0.3%
0 845
 
0.2%
0.962 771
 
0.2%
0.963 716
 
0.2%
0.964 616
 
0.2%
0.965 589
 
0.2%
0.96 560
 
0.1%
0.346 548
 
0.1%
0.396 533
 
0.1%
Other values (2033) 366965
97.8%
(Missing) 777
 
0.2%
ValueCountFrequency (%)
0 845
0.2%
1 × 10-51278
0.3%
2.19 × 10-51
 
< 0.1%
8.66 × 10-51
 
< 0.1%
0.000115 1
 
< 0.1%
0.000179 1
 
< 0.1%
0.000185 9
 
< 0.1%
0.000239 1
 
< 0.1%
0.00028 1
 
< 0.1%
0.000417 9
 
< 0.1%
ValueCountFrequency (%)
1 23
< 0.1%
0.999 6
 
< 0.1%
0.998 3
 
< 0.1%
0.997 7
 
< 0.1%
0.996 5
 
< 0.1%
0.995 8
 
< 0.1%
0.994 16
< 0.1%
0.993 9
 
< 0.1%
0.992 10
< 0.1%
0.991 16
< 0.1%

explicit
Boolean

Distinct2
Distinct (%)< 0.1%
Missing10
Missing (%)< 0.1%
Memory size12.9 MiB
False
316732 
True
58399 
(Missing)
 
10
ValueCountFrequency (%)
False 316732
84.4%
True 58399
 
15.6%
(Missing) 10
 
< 0.1%
2025-04-07T16:58:46.990945image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

track_popularity
Real number (ℝ)

Zeros 

Distinct100
Distinct (%)< 0.1%
Missing10
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean10.273267
Minimum0
Maximum99
Zeros147239
Zeros (%)39.2%
Negative0
Negative (%)0.0%
Memory size2.9 MiB
2025-04-07T16:58:47.119232image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q315
95-th percentile45
Maximum99
Range99
Interquartile range (IQR)15

Descriptive statistics

Standard deviation15.63354
Coefficient of variation (CV)1.5217691
Kurtosis3.33003
Mean10.273267
Median Absolute Deviation (MAD)2
Skewness1.8964838
Sum3853821
Variance244.40758
MonotonicityNot monotonic
2025-04-07T16:58:47.274341image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 147239
39.2%
1 28123
 
7.5%
2 17820
 
4.8%
3 13677
 
3.6%
4 10675
 
2.8%
5 9144
 
2.4%
6 7807
 
2.1%
7 6685
 
1.8%
9 5848
 
1.6%
8 5733
 
1.5%
Other values (90) 122380
32.6%
ValueCountFrequency (%)
0 147239
39.2%
1 28123
 
7.5%
2 17820
 
4.8%
3 13677
 
3.6%
4 10675
 
2.8%
5 9144
 
2.4%
6 7807
 
2.1%
7 6685
 
1.8%
8 5733
 
1.5%
9 5848
 
1.6%
ValueCountFrequency (%)
99 2
 
< 0.1%
98 1
 
< 0.1%
97 1
 
< 0.1%
96 1
 
< 0.1%
95 1
 
< 0.1%
94 5
< 0.1%
93 5
< 0.1%
92 3
 
< 0.1%
91 11
< 0.1%
90 11
< 0.1%

release_year
Real number (ℝ)

Distinct85
Distinct (%)< 0.1%
Missing22
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2013.4102
Minimum1886
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 MiB
2025-04-07T16:58:47.415935image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1886
5-th percentile1990
Q12010
median2017
Q32022
95-th percentile2023
Maximum2023
Range137
Interquartile range (IQR)12

Descriptive statistics

Standard deviation11.814354
Coefficient of variation (CV)0.0058678327
Kurtosis7.7665157
Mean2013.4102
Median Absolute Deviation (MAD)5
Skewness-2.3796072
Sum7.5526842 × 108
Variance139.57897
MonotonicityNot monotonic
2025-04-07T16:58:47.571432image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2023 58986
15.7%
2022 36940
 
9.8%
2021 25990
 
6.9%
2020 24261
 
6.5%
2019 18842
 
5.0%
2018 16790
 
4.5%
2017 15199
 
4.1%
2012 14798
 
3.9%
2013 14732
 
3.9%
2014 14394
 
3.8%
Other values (75) 134187
35.8%
ValueCountFrequency (%)
1886 4
 
< 0.1%
1899 20
 
< 0.1%
1900 14
 
< 0.1%
1901 50
< 0.1%
1912 13
 
< 0.1%
1929 7
 
< 0.1%
1933 51
< 0.1%
1942 11
 
< 0.1%
1946 50
< 0.1%
1948 6
 
< 0.1%
ValueCountFrequency (%)
2023 58986
15.7%
2022 36940
9.8%
2021 25990
6.9%
2020 24261
6.5%
2019 18842
 
5.0%
2018 16790
 
4.5%
2017 15199
 
4.1%
2016 12264
 
3.3%
2015 13349
 
3.6%
2014 14394
 
3.8%

release_month
Categorical

Distinct12
Distinct (%)< 0.1%
Missing22
Missing (%)< 0.1%
Memory size22.6 MiB
January
63888 
November
36295 
October
34846 
September
30097 
July
28528 
Other values (7)
181465 

Length

Max length9
Median length7
Mean length6.3019602
Min length3

Characters and Unicode

Total characters2363985
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowApril
2nd rowApril
3rd rowApril
4th rowApril
5th rowApril

Common Values

ValueCountFrequency (%)
January 63888
17.0%
November 36295
9.7%
October 34846
9.3%
September 30097
8.0%
July 28528
7.6%
June 27756
7.4%
August 27429
7.3%
March 27044
7.2%
May 26022
6.9%
April 25698
6.9%
Other values (2) 47516
12.7%

Length

2025-04-07T16:58:47.718118image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
january 63888
17.0%
november 36295
9.7%
october 34846
9.3%
september 30097
8.0%
july 28528
7.6%
june 27756
7.4%
august 27429
7.3%
march 27044
7.2%
may 26022
6.9%
april 25698
6.9%
Other values (2) 47516
12.7%

Most occurring characters

ValueCountFrequency (%)
e 323033
13.7%
r 287883
12.2%
a 203341
 
8.6%
u 197529
 
8.4%
b 148754
 
6.3%
y 140937
 
6.0%
J 120172
 
5.1%
t 92372
 
3.9%
n 91644
 
3.9%
m 91409
 
3.9%
Other values (16) 666911
28.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2363985
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 323033
13.7%
r 287883
12.2%
a 203341
 
8.6%
u 197529
 
8.4%
b 148754
 
6.3%
y 140937
 
6.0%
J 120172
 
5.1%
t 92372
 
3.9%
n 91644
 
3.9%
m 91409
 
3.9%
Other values (16) 666911
28.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2363985
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 323033
13.7%
r 287883
12.2%
a 203341
 
8.6%
u 197529
 
8.4%
b 148754
 
6.3%
y 140937
 
6.0%
J 120172
 
5.1%
t 92372
 
3.9%
n 91644
 
3.9%
m 91409
 
3.9%
Other values (16) 666911
28.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2363985
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 323033
13.7%
r 287883
12.2%
a 203341
 
8.6%
u 197529
 
8.4%
b 148754
 
6.3%
y 140937
 
6.0%
J 120172
 
5.1%
t 92372
 
3.9%
n 91644
 
3.9%
m 91409
 
3.9%
Other values (16) 666911
28.2%

rn
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size20.8 MiB
1
375141 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters375141
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 375141
100.0%

Length

2025-04-07T16:58:47.845718image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-07T16:58:47.949718image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
1 375141
100.0%

Most occurring characters

ValueCountFrequency (%)
1 375141
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 375141
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 375141
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 375141
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 375141
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 375141
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 375141
100.0%

Interactions

2025-04-07T16:58:13.803975image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:03.390539image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:07.239145image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:11.203135image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:15.100575image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:19.016965image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:22.992952image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:26.771326image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:30.646076image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:34.518444image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:38.663577image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:42.521849image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:46.376668image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:50.113962image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:54.206078image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:58.155019image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:58:01.948664image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:58:06.095149image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:58:09.919037image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:58:13.999997image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:03.596393image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:07.433560image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:11.396987image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:15.304638image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:19.194002image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:23.175770image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:26.974333image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:30.847121image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:34.712049image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:38.870324image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:42.727235image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:46.565662image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:50.339846image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:54.445069image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:58.344127image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:58:02.146867image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:58:06.282692image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:58:10.137243image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:58:14.220178image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:03.801421image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:07.644845image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:11.636985image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:15.506730image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:19.391963image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:23.403171image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:27.180642image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:31.084851image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:34.943202image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:39.091259image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:42.925461image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:46.764999image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:50.535215image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:54.659789image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-04-07T16:57:29.442509image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:33.278633image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:37.391883image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:41.289485image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:45.165228image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:48.896907image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:52.899011image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:56.909941image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:58:00.717548image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:58:04.855021image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:58:08.675968image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:58:12.594652image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:58:16.729928image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:06.208343image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:10.178904image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:14.086300image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:17.945118image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:21.769169image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:25.776850image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:29.649678image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:33.481188image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:37.631374image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:41.495491image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:45.355219image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:49.109477image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:53.102176image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:57.112569image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:58:00.904624image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:58:05.043355image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:58:08.879989image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:58:12.806177image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:58:16.937928image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:06.443170image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:10.381550image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:14.272341image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:18.153896image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:21.967782image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:25.966887image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:29.861012image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:33.676948image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:37.841413image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:41.699686image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:45.544181image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:49.297236image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:53.320138image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:57.320776image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:58:01.132568image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:58:05.255389image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:58:09.084338image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:58:13.023509image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:58:17.160884image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:06.641682image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:10.570659image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:14.472111image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:18.341223image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:22.155305image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:26.166125image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:30.059910image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:33.869047image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:38.029529image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:41.901937image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:45.763945image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:49.481195image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:53.523189image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:57.529821image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:58:01.321002image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:58:05.445979image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:58:09.351881image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:58:13.209647image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:58:17.410898image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:06.834465image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:10.766607image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:14.659002image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:18.528325image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:22.386894image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:26.363431image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:30.245941image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:34.086028image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:38.228539image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:42.102862image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:45.961992image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:49.672815image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:53.707803image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:57.738114image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:58:01.545998image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:58:05.678081image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:58:09.531926image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:58:13.405100image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:58:17.633474image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:07.031506image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:10.976550image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:14.884824image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:18.785881image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:22.571245image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:26.556306image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:30.442987image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:34.278239image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:38.428535image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:42.313578image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:46.160663image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:49.917901image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:53.905286image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:57:57.939606image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:58:01.743990image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:58:05.896694image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:58:09.719765image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-07T16:58:13.596475image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Missing values

2025-04-07T16:58:18.416910image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-07T16:58:19.972223image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-04-07T16:58:24.168384image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

album_idalbum_namealbum_popularityalbum_typeartistsartist_0artist_1artist_2artist_3artist_4artist_idduration_seclabelrelease_datetotal_trackstrack_idtrack_nametrack_numberartist_genresartist_popularityfollowersnamegenre_0genre_1genre_2genre_3genre_4acousticnessanalysis_urldanceabilityduration_msenergyinstrumentalnesskeylivenessloudnessmodespeechinesstempotime_signaturetrack_hreftypeurivalenceexplicittrack_popularityrelease_yearrelease_monthrn
04zH8bbEjLe0Gdbv2FVLjnKSon Of Tabu1album['AB']ABNaNNaNNaNNaN08jcfs5JspUBqM3rbFNUzm92.063About Benjamin Records2023-04-15 00:00:00 UTC85JucnTkqh8uCZGggL1xxvvIntro1[]0133ABNaNNaNNaNNaNNaN0.03050https://api.spotify.com/v1/audio-analysis/5JucnTkqh8uCZGggL1xxvv0.62292064.00.5540.0000015.00.3920-9.5050.00.41472.5464.0https://api.spotify.com/v1/tracks/5JucnTkqh8uCZGggL1xxvvaudio_featuresspotify:track:5JucnTkqh8uCZGggL1xxvv0.382True0.02023.0April1
14zH8bbEjLe0Gdbv2FVLjnKSon Of Tabu1album['AB', '22nd Jim']AB22nd JimNaNNaNNaN08jcfs5JspUBqM3rbFNUzm125.320About Benjamin Records2023-04-15 00:00:00 UTC84VWrKNG3ssyZeboTgQ7Kj1Play It Cool5[]0133ABNaNNaNNaNNaNNaN0.01120https://api.spotify.com/v1/audio-analysis/4VWrKNG3ssyZeboTgQ7Kj10.899125320.00.7010.00000011.00.1530-6.1820.00.138101.5814.0https://api.spotify.com/v1/tracks/4VWrKNG3ssyZeboTgQ7Kj1audio_featuresspotify:track:4VWrKNG3ssyZeboTgQ7Kj10.193True1.02023.0April1
24zH8bbEjLe0Gdbv2FVLjnKSon Of Tabu1album['AB', 'Veeze']ABVeezeNaNNaNNaN08jcfs5JspUBqM3rbFNUzm139.800About Benjamin Records2023-04-15 00:00:00 UTC86JsVrTLJPSOSpgJSnZyfSK3 Peat3[]0133ABNaNNaNNaNNaNNaN0.08150https://api.spotify.com/v1/audio-analysis/6JsVrTLJPSOSpgJSnZyfSK0.782139800.00.7160.00000010.00.1260-8.2950.00.38299.7894.0https://api.spotify.com/v1/tracks/6JsVrTLJPSOSpgJSnZyfSKaudio_featuresspotify:track:6JsVrTLJPSOSpgJSnZyfSK0.835True1.02023.0April1
34zH8bbEjLe0Gdbv2FVLjnKSon Of Tabu1album['AB']ABNaNNaNNaNNaN08jcfs5JspUBqM3rbFNUzm147.777About Benjamin Records2023-04-15 00:00:00 UTC87pZpw1xJWIavCUGqXPPriPAffiliated6[]0133ABNaNNaNNaNNaNNaN0.00413https://api.spotify.com/v1/audio-analysis/7pZpw1xJWIavCUGqXPPriP0.944147778.00.5820.0000001.00.0961-9.2051.00.297105.0104.0https://api.spotify.com/v1/tracks/7pZpw1xJWIavCUGqXPPriPaudio_featuresspotify:track:7pZpw1xJWIavCUGqXPPriP0.252True0.02023.0April1
44zH8bbEjLe0Gdbv2FVLjnKSon Of Tabu1album['AB']ABNaNNaNNaNNaN08jcfs5JspUBqM3rbFNUzm102.600About Benjamin Records2023-04-15 00:00:00 UTC80ylqwc3gcJdJotKd4SfMiuSpringfield8[]0133ABNaNNaNNaNNaNNaN0.01290https://api.spotify.com/v1/audio-analysis/0ylqwc3gcJdJotKd4SfMiu0.527102600.00.6410.0000005.00.0869-11.1730.00.730172.7234.0https://api.spotify.com/v1/tracks/0ylqwc3gcJdJotKd4SfMiuaudio_featuresspotify:track:0ylqwc3gcJdJotKd4SfMiu0.380True0.02023.0April1
54zH8bbEjLe0Gdbv2FVLjnKSon Of Tabu1album['AB']ABNaNNaNNaNNaN08jcfs5JspUBqM3rbFNUzm131.287About Benjamin Records2023-04-15 00:00:00 UTC833prXoje4bkggcsVOP6IT6Decisions2[]0133ABNaNNaNNaNNaNNaN0.02180https://api.spotify.com/v1/audio-analysis/33prXoje4bkggcsVOP6IT60.867131287.00.6100.0000007.00.1310-7.0201.00.327100.9674.0https://api.spotify.com/v1/tracks/33prXoje4bkggcsVOP6IT6audio_featuresspotify:track:33prXoje4bkggcsVOP6IT60.259True1.02023.0April1
64zH8bbEjLe0Gdbv2FVLjnKSon Of Tabu1album['AB']ABNaNNaNNaNNaN08jcfs5JspUBqM3rbFNUzm134.328About Benjamin Records2023-04-15 00:00:00 UTC83FxAeZpDNW3x0UXPwkucplGallery Department4[]0133ABNaNNaNNaNNaNNaN0.02230https://api.spotify.com/v1/audio-analysis/3FxAeZpDNW3x0UXPwkucpl0.896134329.00.6410.0000001.00.3510-6.8381.00.190100.5264.0https://api.spotify.com/v1/tracks/3FxAeZpDNW3x0UXPwkucplaudio_featuresspotify:track:3FxAeZpDNW3x0UXPwkucpl0.301True0.02023.0April1
74zH8bbEjLe0Gdbv2FVLjnKSon Of Tabu1album['AB']ABNaNNaNNaNNaN08jcfs5JspUBqM3rbFNUzm163.422About Benjamin Records2023-04-15 00:00:00 UTC850tZ5mxMfWEvBZ5btMcmmiDead Body7[]0133ABNaNNaNNaNNaNNaN0.32300https://api.spotify.com/v1/audio-analysis/50tZ5mxMfWEvBZ5btMcmmi0.873163422.00.5260.0000001.00.2470-10.0911.00.26093.9994.0https://api.spotify.com/v1/tracks/50tZ5mxMfWEvBZ5btMcmmiaudio_featuresspotify:track:50tZ5mxMfWEvBZ5btMcmmi0.286True0.02023.0April1
87iZ8SfZz0OEF4y5O7SrTXH2Pack0single['AT']ATNaNNaNNaNNaN5Bv0IHYIouKRrJaPKMjepa182.909AT2023-07-07 00:00:00 UTC24IzFhRLg0qK0XSpodSaNBHMellow2[]037ATNaNNaNNaNNaNNaN0.76400https://api.spotify.com/v1/audio-analysis/4IzFhRLg0qK0XSpodSaNBH0.763182909.00.5320.00378010.00.1150-11.0400.00.183126.0984.0https://api.spotify.com/v1/tracks/4IzFhRLg0qK0XSpodSaNBHaudio_featuresspotify:track:4IzFhRLg0qK0XSpodSaNBH0.500True0.02023.0July1
97iZ8SfZz0OEF4y5O7SrTXH2Pack0single['AT']ATNaNNaNNaNNaN5Bv0IHYIouKRrJaPKMjepa190.511AT2023-07-07 00:00:00 UTC221EP4aM1ASi5fn43gPNes1Egomaniac1[]037ATNaNNaNNaNNaNNaN0.01040https://api.spotify.com/v1/audio-analysis/21EP4aM1ASi5fn43gPNes10.695190511.00.7060.0000006.00.1100-8.1811.00.24891.9724.0https://api.spotify.com/v1/tracks/21EP4aM1ASi5fn43gPNes1audio_featuresspotify:track:21EP4aM1ASi5fn43gPNes10.639True0.02023.0July1
album_idalbum_namealbum_popularityalbum_typeartistsartist_0artist_1artist_2artist_3artist_4artist_idduration_seclabelrelease_datetotal_trackstrack_idtrack_nametrack_numberartist_genresartist_popularityfollowersnamegenre_0genre_1genre_2genre_3genre_4acousticnessanalysis_urldanceabilityduration_msenergyinstrumentalnesskeylivenessloudnessmodespeechinesstempotime_signaturetrack_hreftypeurivalenceexplicittrack_popularityrelease_yearrelease_monthrn
3751312rU7u7C2v5i45MFVxx7xG1Taylor Swift (Big Machine Radio Release Special)65album['Taylor Swift']Taylor SwiftNaNNaNNaNNaN06HL4z0CvFAxyc27GXpf02204.706Big Machine Records, LLC2006-10-24 00:00:00 UTC307snx7w5WZLHovS0xC45ZfPTeardrops On My Guitar - Radio Single Remix6['pop']10095859165Taylor SwiftpopNaNNaNNaNNaN0.33100https://api.spotify.com/v1/audio-analysis/7snx7w5WZLHovS0xC45ZfP0.626204707.00.4400.00000010.00.1460-6.9421.00.023299.9624.0https://api.spotify.com/v1/tracks/7snx7w5WZLHovS0xC45ZfPaudio_featuresspotify:track:7snx7w5WZLHovS0xC45ZfP0.2590False60.02006.0October1
3751324hDok0OAJd57SGIT8xuWJHFearless (Taylor'S Version)89album['Taylor Swift']Taylor SwiftNaNNaNNaNNaN06HL4z0CvFAxyc27GXpf02200.575Taylor Swift2021-04-09 00:00:00 UTC260k0vFacOHNuArLWMiH60p7Tell Me Why (Taylor’S Version)8['pop']10095859165Taylor SwiftpopNaNNaNNaNNaN0.02220https://api.spotify.com/v1/audio-analysis/0k0vFacOHNuArLWMiH60p70.578200576.00.9090.0000007.00.3330-3.6691.00.0628100.0234.0https://api.spotify.com/v1/tracks/0k0vFacOHNuArLWMiH60p7audio_featuresspotify:track:0k0vFacOHNuArLWMiH60p70.5410False68.02021.0April1
3751331KlU96Hw9nlvqpBPlSqcTVRed (Deluxe Edition)62album['Taylor Swift']Taylor SwiftNaNNaNNaNNaN06HL4z0CvFAxyc27GXpf02230.133Big Machine Records, LLC2012-10-22 00:00:00 UTC223bIxTsfeNMO7Nt2J3EUKrA226['pop']10095859165Taylor SwiftpopNaNNaNNaNNaN0.00215https://api.spotify.com/v1/audio-analysis/3bIxTsfeNMO7Nt2J3EUKrA0.658230133.00.7290.0013007.00.0752-6.5611.00.0378104.0074.0https://api.spotify.com/v1/tracks/3bIxTsfeNMO7Nt2J3EUKrAaudio_featuresspotify:track:3bIxTsfeNMO7Nt2J3EUKrA0.6680False58.02012.0October1
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